{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARCH Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This setup code is required to run in an IPython notebook_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "seaborn.set_style('darkgrid')\n",
    "plt.rc(\"figure\", figsize=(16, 6))\n",
    "plt.rc(\"savefig\", dpi=90)\n",
    "plt.rc(\"font\",family=\"sans-serif\")\n",
    "plt.rc(\"font\",size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These examples will all make use of financial data from Yahoo! Finance.  This data set can be loaded from `arch.data.sp500`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "import arch.data.sp500\n",
    "\n",
    "st = dt.datetime(1988, 1, 1)\n",
    "en = dt.datetime(2018, 1, 1)\n",
    "data = arch.data.sp500.load()\n",
    "market = data['Adj Close']\n",
    "returns = 100 * market.pct_change().dropna()\n",
    "ax = returns.plot()\n",
    "xlim = ax.set_xlim(returns.index.min(), returns.index.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying Common Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The simplest way to specify a model is to use the model constructor `arch.arch_model` which can specify most common models.  The simplest invocation of `arch` will return a model with a constant mean, GARCH(1,1) volatility process and normally distributed errors.\n",
    "\n",
    "\n",
    "$$ r_t  =  \\mu + \\epsilon_t$$\n",
    "\n",
    "$$\\sigma^2_t   =  \\omega + \\alpha \\epsilon_{t-1}^2 + \\beta \\sigma_{t-1}^2 $$\n",
    "\n",
    "$$\\epsilon_t  =  \\sigma_t e_t,\\,\\,\\, e_t  \\sim  N(0,1) $$\n",
    "\n",
    "\n",
    "The model is estimated by calling `fit`.  The optional inputs `iter` controls the frequency of output form the optimizer, and `disp` controls whether convergence information is returned.  The results class returned offers direct access to the estimated parameters and related quantities, as well as a `summary` of the estimation results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GARCH (with a Constant Mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default set of options produces a model with a constant mean, GARCH(1,1) conditional variance and normal errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      5,   Func. Count:     39,   Neg. LLF: 6942.159596737265\n",
      "Iteration:     10,   Func. Count:     72,   Neg. LLF: 6936.718529994181\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 6936.718476989043\n",
      "            Iterations: 11\n",
      "            Function evaluations: 79\n",
      "            Gradient evaluations: 11\n",
      "                     Constant Mean - GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:              Adj Close   R-squared:                      -0.001\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                 -0.001\n",
      "Vol Model:                      GARCH   Log-Likelihood:               -6936.72\n",
      "Distribution:                  Normal   AIC:                           13881.4\n",
      "Method:            Maximum Likelihood   BIC:                           13907.5\n",
      "                                        No. Observations:                 5030\n",
      "Date:                Mon, Mar 02 2020   Df Residuals:                     5026\n",
      "Time:                        11:53:28   Df Model:                            4\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu             0.0564  1.149e-02      4.906  9.302e-07 [3.384e-02,7.887e-02]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega          0.0175  4.683e-03      3.738  1.854e-04 [8.328e-03,2.669e-02]\n",
      "alpha[1]       0.1022  1.301e-02      7.852  4.105e-15   [7.665e-02,  0.128]\n",
      "beta[1]        0.8852  1.380e-02     64.125      0.000     [  0.858,  0.912]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "from arch import arch_model\n",
    "\n",
    "am = arch_model(returns)\n",
    "res = am.fit(update_freq=5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`plot()` can be used to quickly visualize the standardized residuals and conditional volatility.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res.plot(annualize='D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GJR-GARCH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional inputs can be used to construct other models.  This example sets `o` to 1, which includes one lag of an asymmetric shock which transforms a GARCH model into a GJR-GARCH model with variance dynamics given by \n",
    "\n",
    "$$\n",
    "\\sigma^2_t   =  \\omega + \\alpha \\epsilon_{t-1}^2 + \\gamma \\epsilon_{t-1}^2 I_{[\\epsilon_{t-1}<0]}+ \\beta \\sigma_{t-1}^2 \n",
    "$$\n",
    "\n",
    "where $I$ is an indicator function that takes the value 1 when its argument is true.\n",
    "\n",
    "The log likelihood improves substantially with the introduction of an asymmetric term, and the parameter estimate is highly significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Constant Mean - GJR-GARCH Model Results                    \n",
      "==============================================================================\n",
      "Dep. Variable:              Adj Close   R-squared:                      -0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
      "Vol Model:                  GJR-GARCH   Log-Likelihood:               -6822.88\n",
      "Distribution:                  Normal   AIC:                           13655.8\n",
      "Method:            Maximum Likelihood   BIC:                           13688.4\n",
      "                                        No. Observations:                 5030\n",
      "Date:                Mon, Mar 02 2020   Df Residuals:                     5025\n",
      "Time:                        11:53:28   Df Model:                            5\n",
      "                                  Mean Model                                 \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "mu             0.0175  1.145e-02      1.529      0.126 [-4.936e-03,3.995e-02]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega          0.0196  4.051e-03      4.830  1.362e-06  [1.163e-02,2.751e-02]\n",
      "alpha[1]   3.3117e-10  1.026e-02  3.227e-08      1.000 [-2.011e-02,2.011e-02]\n",
      "gamma[1]       0.1831  2.266e-02      8.079  6.543e-16      [  0.139,  0.227]\n",
      "beta[1]        0.8922  1.458e-02     61.200      0.000      [  0.864,  0.921]\n",
      "=============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "am = arch_model(returns, p=1, o=1, q=1)\n",
    "res = am.fit(update_freq=5, disp='off')\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TARCH/ZARCH"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TARCH (also known as ZARCH) model the _volatility_ using absolute values.  This model is specified using `power=1.0` since the default power, 2, corresponds to variance processes that evolve in squares.\n",
    "\n",
    "The volatility process in a TARCH model is given by \n",
    "\n",
    "$$\n",
    "\\sigma_t  =  \\omega + \\alpha \\left|\\epsilon_{t-1}\\right| + \\gamma \\left|\\epsilon_{t-1}\\right| I_{[\\epsilon_{t-1}<0]}+ \\beta \\sigma_{t-1} \n",
    "$$\n",
    "\n",
    "More general models with other powers ($\\kappa$) have volatility dynamics given by \n",
    "\n",
    "$$\n",
    "\\sigma_t^\\kappa   = \\omega + \\alpha \\left|\\epsilon_{t-1}\\right|^\\kappa + \\gamma \\left|\\epsilon_{t-1}\\right|^\\kappa I_{[\\epsilon_{t-1}<0]}+ \\beta \\sigma_{t-1}^\\kappa \n",
    "$$\n",
    "\n",
    "where the conditional variance is $\\left(\\sigma_t^\\kappa\\right)^{2/\\kappa}$.\n",
    "\n",
    "The TARCH model also improves the fit, although the change in the log likelihood is less dramatic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      5,   Func. Count:     49,   Neg. LLF: 6807.734808859357\n",
      "Iteration:     10,   Func. Count:     88,   Neg. LLF: 6799.179902576483\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 6799.17852245191\n",
      "            Iterations: 14\n",
      "            Function evaluations: 116\n",
      "            Gradient evaluations: 13\n",
      "                  Constant Mean - TARCH/ZARCH Model Results                   \n",
      "==============================================================================\n",
      "Dep. Variable:              Adj Close   R-squared:                      -0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
      "Vol Model:                TARCH/ZARCH   Log-Likelihood:               -6799.18\n",
      "Distribution:                  Normal   AIC:                           13608.4\n",
      "Method:            Maximum Likelihood   BIC:                           13641.0\n",
      "                                        No. Observations:                 5030\n",
      "Date:                Mon, Mar 02 2020   Df Residuals:                     5025\n",
      "Time:                        11:53:28   Df Model:                            5\n",
      "                                  Mean Model                                 \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "mu             0.0143  1.091e-02      1.311      0.190 [-7.080e-03,3.570e-02]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega          0.0258  4.100e-03      6.299  2.986e-10  [1.779e-02,3.386e-02]\n",
      "alpha[1]   3.0844e-09  9.156e-03  3.369e-07      1.000 [-1.794e-02,1.794e-02]\n",
      "gamma[1]       0.1707  1.601e-02     10.664  1.499e-26      [  0.139,  0.202]\n",
      "beta[1]        0.9098  9.672e-03     94.066      0.000      [  0.891,  0.929]\n",
      "=============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "am = arch_model(returns, p=1, o=1, q=1, power=1.0)\n",
    "res = am.fit(update_freq=5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Student's T Errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Financial returns are often heavy tailed, and a Student's T distribution is a simple method to capture this feature.  The call to `arch` changes the distribution from a Normal to a Students's T.\n",
    "\n",
    "The standardized residuals appear to be heavy tailed with an estimated degree of freedom near 10.  The log-likelihood also shows a large increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration:      5,   Func. Count:     54,   Neg. LLF: 6726.104807616888\n",
      "Iteration:     10,   Func. Count:     99,   Neg. LLF: 6722.153431139288\n",
      "Optimization terminated successfully.    (Exit mode 0)\n",
      "            Current function value: 6722.151184378068\n",
      "            Iterations: 13\n",
      "            Function evaluations: 121\n",
      "            Gradient evaluations: 12\n",
      "                     Constant Mean - TARCH/ZARCH Model Results                      \n",
      "====================================================================================\n",
      "Dep. Variable:                    Adj Close   R-squared:                      -0.000\n",
      "Mean Model:                   Constant Mean   Adj. R-squared:                 -0.000\n",
      "Vol Model:                      TARCH/ZARCH   Log-Likelihood:               -6722.15\n",
      "Distribution:      Standardized Student's t   AIC:                           13456.3\n",
      "Method:                  Maximum Likelihood   BIC:                           13495.4\n",
      "                                              No. Observations:                 5030\n",
      "Date:                      Mon, Mar 02 2020   Df Residuals:                     5024\n",
      "Time:                              11:53:28   Df Model:                            6\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu             0.0323  2.299e-03     14.037  9.214e-45 [2.776e-02,3.677e-02]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega          0.0201  3.492e-03      5.745  9.185e-09  [1.322e-02,2.691e-02]\n",
      "alpha[1]   2.4040e-09  8.223e-03  2.924e-07      1.000 [-1.612e-02,1.612e-02]\n",
      "gamma[1]       0.1721  1.512e-02     11.386  4.931e-30      [  0.143,  0.202]\n",
      "beta[1]        0.9139  9.578e-03     95.415      0.000      [  0.895,  0.933]\n",
      "                              Distribution                              \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "nu             7.9550      0.880      9.036  1.627e-19 [  6.229,  9.680]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "am = arch_model(returns, p=1, o=1, q=1, power=1.0, dist='StudentsT')\n",
    "res = am.fit(update_freq=5)\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fixing Parameters\n",
    "In some circumstances, fixed rather than estimated parameters might be of interest.  A model-result-like class can be generated using the `fix()` method.  The class returned is identical to the usual model result class except that information about inference (standard errors, t-stats, etc) is not available. \n",
    "\n",
    "In the example, I fix the parameters to a symmetric version of the previously estimated model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Constant Mean - TARCH/ZARCH Model Results                      \n",
      "=====================================================================================\n",
      "Dep. Variable:                     Adj Close   R-squared:                          --\n",
      "Mean Model:                    Constant Mean   Adj. R-squared:                     --\n",
      "Vol Model:                       TARCH/ZARCH   Log-Likelihood:               -6908.93\n",
      "Distribution:       Standardized Student's t   AIC:                           13829.9\n",
      "Method:            User-specified Parameters   BIC:                           13869.0\n",
      "                                               No. Observations:                 5030\n",
      "Date:                       Mon, Mar 02 2020                                         \n",
      "Time:                               11:53:28                                         \n",
      "      Mean Model     \n",
      "=====================\n",
      "                 coef\n",
      "---------------------\n",
      "mu             0.0235\n",
      "   Volatility Model  \n",
      "=====================\n",
      "                 coef\n",
      "---------------------\n",
      "omega          0.0100\n",
      "alpha[1]       0.0600\n",
      "gamma[1]       0.0000\n",
      "beta[1]        0.9382\n",
      "     Distribution    \n",
      "=====================\n",
      "                 coef\n",
      "---------------------\n",
      "nu             8.0000\n",
      "=====================\n",
      "\n",
      "Results generated with user-specified parameters.\n",
      "Std. errors not available when the model is not estimated, \n"
     ]
    }
   ],
   "source": [
    "fixed_res = am.fix([0.0235, 0.01, 0.06, 0.0, 0.9382, 8.0])\n",
    "print(fixed_res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(729759.0, 737059.0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.concat([res.conditional_volatility, fixed_res.conditional_volatility],\n",
    "               1)\n",
    "df.columns = ['Estimated', 'Fixed']\n",
    "subplot = df.plot()\n",
    "subplot.set_xlim(xlim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Model From Components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models can also be systematically assembled from the three model components:\n",
    "\n",
    "* A mean model (`arch.mean`)\n",
    "    * Zero mean (`ZeroMean`) - useful if using residuals from a model estimated separately\n",
    "    * Constant mean (`ConstantMean`) - common for most liquid financial assets\n",
    "    * Autoregressive (`ARX`) with optional exogenous regressors\n",
    "    * Heterogeneous (`HARX`) autoregression with optional exogenous regressors\n",
    "    * Exogenous regressors only (`LS`)\n",
    "* A volatility process (`arch.volatility`)\n",
    "    * ARCH (`ARCH`)\n",
    "    * GARCH (`GARCH`)\n",
    "    * GJR-GARCH (`GARCH` using `o` argument) \n",
    "    * TARCH/ZARCH (`GARCH` using `power` argument set to `1`)\n",
    "    * Power GARCH and Asymmetric Power GARCH (`GARCH` using `power`)\n",
    "    * Exponentially Weighted Moving Average Variance with estimated coefficient (`EWMAVariance`)\n",
    "    * Heterogeneous ARCH (`HARCH`)\n",
    "    * Parameterless Models\n",
    "        * Exponentially Weighted Moving Average Variance, known as RiskMetrics (`EWMAVariance`)\n",
    "        * Weighted averages of EWMAs, known as the RiskMetrics 2006 methodology (`RiskMetrics2006`)\n",
    "* A distribution (`arch.distribution`)\n",
    "    * Normal (`Normal`)\n",
    "    * Standardized Students's T (`StudentsT`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first choice is the mean model.  For many liquid financial assets, a constant mean (or even zero) is adequate.  For other series, such as inflation, a more complicated model may be required.  These examples make use of Core CPI downloaded from the [Federal Reserve Economic Data](https://fred.stlouisfed.org/) site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZGSUiIqISwzBKRKrKHOvSeswkXeDoAUaszmXOGHXmccYoABi1Gmg1AgLHDIXKVEYze0aNOhFGrQa+CL/3REREVBoYRolIVUeOdTm+MmpIBydWRpVJugDy3jMqCAKsBu1x03Tjx4RRQGnV9UZZGSUiIqLSwDBKRKrq9UbgsuphOuZYF0AJTha9yD2jALxhJRTm26YLAFbD8d/TxDFtuoDSqss2XSIiIioVDKNEpKp+T2Ta4UUZFoOWlVEcqYyqEkb1WgSPbdNNZgYYCdnHnCYd23SJiIioZDCMEpGq+ryzhFG9yDAKYCKkTL+ts+Q3TReYvjKaadM1HNOm62ObLhEREZUIhlEiUk32WJdpJulmTLe/sRqNBeMwaDWwGo5vZ54vq0GL4DHf05gkAzhmzyjbdImIiKiEMIwSkWpOdKxLhrJnlJXR8VAMLqsegiDM/uRZWAzHt+lmjnbRi1Mro/6ohGRKzvs9iYiIiPLFMEpEqjnRsS4ZSpsuK6PjoTjqVWjRBQDrNEOhsgOMjqqMOk06yAACHCBFREREJYBhlIhUc6JjXTKsBi1CrIxiLBhHvcWgyrWsBi3C8SRS8pGKZ/ac0SmVUS2AI2ecEhERERUTwygRqeZEx7pksDKqGA/G4bKqVBk1aCEDCB81GCo+XRg1KpN7uW+UiIiISgHDKBGpZrZjXQAlOMWTcjYsVaNQXEI4kVQvjOqV8H90q252z+gxbboA4IvywwAiIiIqPoZRIlLNbMe6AEplFKjufYtjQeVYl3oVK6MApgwxyrbpatmmS0RERKWJYZSIVDGXY12AI+dqZs7ZrEbjmTCq1gAjw/SVUVEAtJoj03rZpktERESlhGGUiFSRPdblBJN0AcBlVYb2ZKqD1Wg8HcRdKg4wAjDlrNG4JE+pigJKVVrUCGzTJSIiopLAMEpEqsge6zJLZdSdbk0dDcYKvqZSNZb+2lVr09Uf36YbT6amDC8CAEEQ4DTpWBklIiKikjCnMLp161bcdNNNOP/889HV1YUnnnhiyt/LsoxNmzbhvPPOw5o1a/D5z38e+/btK8iCiag0zeVYF+BIm+54FVdGx4JxmHSa7P7ZfE3bpiuljquMAoDDqOWeUSIiIioJcwqj4XAYnZ2duP3222E0Ht+C95Of/AQPPfQQ7rjjDvzmN79BbW0trrvuOgSDQdUXTESlaS7HugCATtSg1qyr6spo92gQ7bVmCIIw+5Pn4MgAoyNhNDZNZRQAHCYd23SJiIioJMwpjG7YsAHf/OY3cckll0CjmfoSWZbx6KOP4sYbb8TFF1+Mzs5O/Mu//AtCoRCefvrpgiyaiEpPvycya1U0w2U1VO2e0biUwq7hAE5Z5FDtmgatBqJGQPCYc0anq4yyTZeIiIhKRd57Rvv7+zE2NoZzzz03+5jRaMQZZ5yBbdu25Xt5IioTfd4IFs85jOqrrjKakmXc9ed9ePy9QcSkFE5tUS+MCoIAq148bpquYbrKqFHLyigRERGVBG2+FxgbGwMA1NfXT3m8rq4Oo6Ojs75eFAU4neac3lsUNTm/liiD91H+AlHlWJflTfY5fS8X1ZqxeyRYcd/3E91Lg94Ifvv+UPbPF6xshFOlo10AwG7SIS4j+/4pQYDZqD1uPQ1OE3yRBBwOk2ptwqQ+/lwitfBeIjXwPqJCyTuMZuT6S00yKcPrDef0WqfTnPNriTJ4H+Xvg5EAAMBlFOf0vXToRUyG4hgdD07bSlquTnQv7er1ZP93e60JYkKC16tehdKqFzHuj2bfPxxNQKsRjluPQQCklIyB0UB2rymVHv5cIrXwXiI18D6ifLlctmkfz/u3QJfLBeBIhTRjYmLiuGopEVWmuR7rkpE53iVz3mY1yHyPPrrCjc+etkj16x+7Dzd2gj2jAOCLct8oERERFVfeYbSlpQUulwtvvPFG9rFYLIa3334bp556ar6XJ6IykDk3s8FmmNPz662GKa+rBv2eCPSigH/8aBeuWNus+vXdx+zDne6cUUCZpgsA3gj3jRIREVFxzalHKxQKobe3FwCQSqUwODiIPXv2wOFwoLm5Gddeey0eeOABdHR0oL29Hffffz/MZjMuu+yygi6eiEpDKD3F1ayfW9uny1KNldEIFjlM0BRon2aDzQB/VEIkkYRJJyIupWCY4ZxRAJyoS0REREU3p98cd+7ciWuvvTb7502bNmHTpk345Cc/ibvuugs33HADYrEY7rzzTvh8PqxduxYPPfQQrFZrwRZORKUjHE/CoNVAq5lb0Mq2ilZRIOr3RtHiPP6cZrW401Xp0UAMbbVmxJPytJVRtukSERFRqZhTGF2/fj26u7tn/HtBEHDzzTfj5ptvVm1hRFQ+wvEkLHpxzs93ZANRdbSKyrKMPm8EZ7Y5C/YemRbp0WA6jM6wZzT7vWebLhERERVZ5YyxJKKiCcUlmOcRRg1aDUw6DbxVUhkdD8URk1JomeM5rLlwp/fhDvtj6PVEZtwzajNoIQBV870nIiKi0sW5/kSUt3A8CbNu7mEUABxGXdW06fZ6IgCA1gK26brSE4of2dKHXs9eyMC0lVFRI8Bu1FbN956IiIhKF8MoEeUtnJhfmy6g7F2slomuu4eVc1i73IXbR2/UiXAYtTicDr4AYJimMgoorbrV0iJNREREpYttukSUt3A8OedJuhkOk7ZqhujsGg5gkcOIGrO+oO/jPuZonekqo5nnDfqiBV0LERER0WwYRokob6F4cl57RoFMZbSyw6gsy5BlGTsG/VjdZCv4+2WGGGWmGs8URpfXW9AzHkIyJRd8TUREREQzYRgloryFcwijyp7RymsVTckyntw+BH80gf/7bDe+8Nh7GA3GsarJXvD3bnGaYDWI+Ju1TQCAwAyV5+UuC2JSCn3eyLR/T0RERLQQuGeUiPI236NdAKUyGohJkFLynM8nLQc7hwL43vP78KttA9g/Hs4+fvICVEZvOHsxPrW2Cb2eCP572yDiyekrn53pvat7R4NorzUXfF1ERERE02FllIjykpJlhBM5TNM1KZ+F+Sts3+iu9LCi/eNhNNoMuOqUZjiMWnS6Cje8KMNu1KGt1ozzOmpx58e6cN361mmft6TWDFEjYN9YqOBrIiIiIpoJK6NElJdIIgkAOe0ZBQBfREJtgQf7LKRdQ364rHp8+tRFOGWRHWua7fi7DR0z7t8sBEEQ8NEVDTP+vV6rwZJaM8MoERERFRXDKBHlJRxXwuh823QdRiWMVtoQo93DAaxqtOFvzzxSlTRoS68NudNtwWsHJjHsj6LRXrjzT4mIiIhmwjZdIspLKJ6pjM7vs60jldHKCaPecBx93ihWNhZ+f2i+rl7XgpQs46bHtyOarm4TERERLSSGUSLKSzieW5tuZs9oJVVGdwz6AQCryiCMdrmt+PaHl2PAF8X+cbbrEhER0cJjGCWivOTapputjEYr53iXQ+lQ11FvKfJK5qat1gQAGAvGi7wSIiIiqkYMo0SUl1COlVGjToReFCqqTXc8GIdGAGrSQbvUuawGAMAowygREREVAcMoEeUlnFAqm/M92gVQ9pmGK2i/4kQoBqdJB7FMzk2tNesgCsB4KFbspRAREVEVYhglorzk2qYLAGadJns0TCUYC8RQZymfY2o0goA6i56VUSIiIioKhlEiyks4x2m6mddkXl8JxkNx1JrLo0U3w20zYDzIyigREREtPIZRIspLKJ6EAMCkm/+PE5NOrKjK6ESwvCqjAFDPyigREREVCcMoEeUlHE/CrBchCPPfJ2nWayqmMirLMsaCcdSayyuMuq0GjAfj+MfnuvGHncPFXg4RERFVEYZRIspLJozmwqQTK2aAUSieRFxKlV2brsuqRyAm4Y+7RvDr9waLvRwiIiKqIgyjRJSXUFzKaZIuoAw9ilRIZXQipLS6llubbuZ4FwDYMxLEZJgtu0RERLQwGEaJKC/BWBJ24/yHFwGZymhK5RUVx2RYOS+1rszadF1WZb02g/JvuPmwp5jLISIioirCMEpEeQnEJFgNuYVRs75yBhhlKqO1lvJq03XblMroZ05rhsOoxVuHGEaJiIhoYeT2GyQRUVogJmGRw5jTa006ETEpBSklQ6uZ/wCkUpJpby23Nt22GhPuuLgTG5fXo9cTwVuHPEjJMjQ5DKQiIiIimg9WRokoL8GYBFuObbqZwUeVsG90IpyARgAcxvKqjAqCgI+vboTVoMVZ7TWYDCewbyxU7GURERFRFWAYJaKcybKcX5tuevBROU/UlWUZANAzFoLLZoBYxhXes9pqAICtukRERLQgGEaJKGcxKYVEUs4Ov5mvcq+M+iIJXP7TLbjjmQ/wyv4JfOq0lmIvKS/1VgOWuyx489BksZdCREREVYBhlIhyFoxJAACbIfdzRgEgVKaV0Se2D2HQH8Nze0ZhM2jxxXPai72kvJ3VVoP3B/yIS5Ux5ZiIiIhKFwcYEVHOAjElROYzTRcoz8poIpnC49sGceZiJ85qr8Eipwl2kw7eWKLYS8tLi9MIKSXDF01MOYOUiIiISG0Mo0SUs0CmMprnAKNy3DP6Tp8X46E4vvNXy3HB0rpiL0c1tvQAJl9UYhglIiKigmKbLhHlLBtGc6yMZtp0y7EyOuiLAgC63NYir0Rd9vQHC4GoVOSVEBERUaVjGCWinAXTgSXfabrluGd0OBCDKAD1ZXau6GwyYdQfLe92YyIiIip9DKNElLNMZbQa94wO+2Nwl/lRLtM5EkZZGSUiIqLCYhglopzl26ZrLONzRof9UTTajcVehursBmXPKMMoERERFZoqYTSZTOLee+/Fxo0bcfLJJ2Pjxo34t3/7N0gSf5khqmTBmAS9KMCgze1HiVajvLYsK6OBGBptlTfgx2IQoRHYpktERESFp8o03Z/85Cd47LHHcNddd6GzsxPd3d34+7//e+j1enz1q19V4y2IqAQFYlLOLboZZp1YdpXRZErGaCCGRnvlhVGNIMBm0LIySkRERAWnShjdtm0bLrzwQmzcuBEA0NLSgo0bN2L79u1qXJ6ISlQgmsy5RTfDpBcRLrPK6HgojqSMiqyMAsq+UYZRIiIiKjRV2nTXrVuHzZs3Y//+/QCAnp4evPXWW7jgggvUuDwRlahgTMr5jNEMs05EpMwqo8N+5ViXStwzCgB2ow7+GMMoERERFZYqldEbbrgBoVAIl156KURRhCRJuOmmm3DNNdfM+lpRFOB0mnN6X1HU5PxaogzeR7kLJ1OosRjy+v7ZTDrEUyirfwN/rxcAsHyRc8q6K+VeqrUa4IskKuJrKVeVci9R8fFeIjXwPqJCUSWMPvPMM3jyySfxgx/8AMuWLcOePXvwz//8z2hpacGVV155wtcmkzK83nBO7+t0mnN+LVEG76Pc+UJxNFj0eX3/rDoNhgPRsvo3ODAcAACYkZqy7kq5l8xaAYdCsYr4WspVpdxLVHy8l0gNvI8oXy6XbdrHVQmjd999N774xS/i0ksvBQB0dXVhcHAQDz744KxhlIjKVyAm5b1ntMFmwPuDfpVWtDCG/VHYjVpY9Kr8CC05dqOOe0aJiIio4FTZMxqNRiGK4pTHRFFEKpVS4/JEVKKCKkzTbbAZ4I9KZTXEqFKPdcmwpQcYpWS52EshIiKiCqbKx/oXXnghHnzwQbS0tGTbdB9++GFcfvnlalyeiEpQTEohnpRhM4izP/kEMkOARgIxLKkrj/0ow/4Ymh2VObwIABxGLWQAoVgy7wFVRERERDNR5beMf/iHf8APf/hD/NM//RMmJibgcrlw1VVX8YxRogoWSE9bzTesNKQrjCOBaPmE0UAUp7U4ir2Mgsm0XvuiCYZRIiIiKhhVfsuwWq24/fbbcfvtt6txOSIqA8H0nkI19owCSrWxHARjEoKxJBrtldumazfqABz5wIGIiIioEFTZM0pE1ScTVPLdM+q26iFAadMtB5nQXKlnjAJKmy4A+CKJIq+EiIiIKhnDKBHlJNumm2cY1Yoa1Fv15RNGA1EAqOgBRk6zUhn1MIwSERFRATGMElFOgiqFUUBp1R0ulzCarYxWbhitzYTRMMMoERERFQ7DKBHlJNumq8KAm0aboYwqozFoNQLqLPpiL6VgbAYtRI2ASYZRIiIiKiCGUSXjvT4AACAASURBVCLKSUClAUYA4E6HUbkMzrUc9kfRYDNAIwjFXkrBCIKAWrMOXoZRIiIiKiCGUSLKSSCWhF4UYNDm/2OkwWZATErBFyn96a29nggWVfAZoxk1Jh0mw/FiL4OIiIgqGMMoEeUkGJPynqSbkZlMW+qtusmUjAMTYSxzWYq9lIKrNes5wIiIiIgKimGUiHISiEmqtOgCR501WuJhtM8TQUxKYXkVhNEas457RomIiKigGEaJKCcBFSujmTA6kj42pVTtGw8BAJbXW4u8ksKrMevgYZsuERERFRDDKBHlJKhiZbTWrINOFEq+TbdnLAhRANrrzMVeSsHVmHSIJFKIJJLFXgoRERFVKIZRIspJIKpeZVQjCHBbS/94l31jIbTVmlUZ2lTqas3K0TU8a5SIiIgKpfJ/oyKiggjEJNiMomrXayiDs0Z7xkNVsV8UUNp0AbBVl4iIiAqGYZSIcqJmmy6ghNFhf+mG0WBMwpA/hmX11RFGazNhlBN1iYiIqEAYRolo3mJSCvGkrFqbLgA02g0YC8aQTMmqXVNNPWPp4UWuyh9eBAA16TZdTtQlIiKiQmEYJaJ5C8YkAFC9MpqUgYlQabaF7k2H0Wo4YxQ4qjLKMEpEREQFwjBKRPMWKEAYXVqnhLz7XjtYktXRnvEgHEYt3FZ9sZeyIAxaDUSNgFBcKvZSiIiIqEIxjBLRvGUqo1ajemH0lBYHbjynDX/cPYo/7hpR7bpq6RkLYZnLAkEQir2UBSEIAsw6EeE4j3YhIiKiwmAYJaJ5K0RlFACuP2sx6ix6vNPvVfW6+UrJMnrGQ1UzvCjDpNPwnFEiIiIqGIZRIpq3QLQwYVQQBJzcZMPOoQAeeP0QvvG7napeP1cHJsKIJFJVc6xLhkknIpJIFXsZREREVKEYRolo3o4MMFLvnNGM1U129Hoi+NW7A9ja64UsT79/VErJ+NeX9uPlngnV13CsJ7cPQScKOK+jruDvVUrMepGVUSIiIioYhlEimrdATAkoah7tkrG6yQYACMWTiEmpGY8WeafPi/96dwDfemoXvvunvQUbtBOKS3h61wg+0ulCnaU6hhdlGLlnlIiIiAqIYZSI5i0Qk6ATBRi06v8IWdFgg+aoGUHD/ui0z/tz9xjMOhHXntGCP+waxq1P7lJ9LQDwx10jCMWT+PSpzQW5fikz61gZJSIiosJRv6xBRBUvGJNgM2gLMlnWrBdx4fJ6JFMy/tIzgSF/DKuapj5HSqbw0r5xnL+0Fjdf0AEpJePX7w0imZIhatRbU0qW8fi2QaxqtGFVk12165YLE8MoERERFRAro0QLJC6lcHAiXOxlqCIQlQrSoptx11+vxP+9uAsAMDRNZfT9QT98UQkf7nQBANpqTEgkZYwFYye87luHJtE9GpzzOrYc9uCwJ4KrqrAqCijTdNmmS0RERIXCMEq0QH63fQhX/fxtfOcPuxGXyntCaSBdGS0kq0GERS/isCeC+147CG/kyN7RfWMhAMCaZqVa2ewwAgAGZ2jpzbjzT3tx/2uH5ryG5z4Yg8OoxUfSobfaKAOMyvteJSIiotLFMEq0QA57IgCAP+8dx6sHCj8BtpCCCxBGBUFAo92A5/aM4uHNfXhuzyiCMQmBqITDk2FYDSJqzToAQLPDBAAY8M4cRv3RBMaCcfR7I3New97RIFY22qAvwN7YcsA2XSIiIiok7hklUtkzu0fwxsFJfPfSFVMeHwvG0Oo0YtAfQ/doMNtiWo4CMQkNNmPB36fJbsT+caW1+b0BH/7SMw5ACarttebsntUmuwECgEHfzGE00yI96I/OaW+plFTaqs9ur1HhKylPJp0IKSUjkUxBJ1ZnICciIqLC4W8XRCr7/c5h/OmDMQSiU48aGQnEsMhpwpJaM/aOhoq0OnUEYknYjOqfMXqsRpsh+783H/bgnT4f3hvwo2cshLYaU/bvdKIGbpvhhG26mTA6097SlCwjelQV8LAnAiklY5nLosaXUpZMeuXfmPtGiYiIqBAYRolUlEimsHMoAADYNz51UM5oMI4GqwGdbgv2js19iE4pCsYkWPWFb6xosivV14u6XAimzzZNpmR4Iwm01ZqnPLfZYTxhm+7BySPDo/qned6vtw3iwz9+A3/cNQIA6EnvS11eb83viyhjZp3ynwi26hIREVEhMIwSqah7NIhYejhRJswASsvnZCgOt02PTpcVY8E4JsPxYi0zLzEphZiUgs1Y+DB68Qo3vnJeO647azEAoMVpzJ5B2n5MGF3kMM5aGXWk1zzgO37f6LYBH+JJGf/4XDf2jASwbzwEUSOgrdZ03HOrhUmnVEY5xIiIiIgKgWGUSEXvDfgBAEatBnuPCqPjoThkAG6rAV1updK2dx5HjJSSYExpPy7k0S4ZDTYDrlu/GEvrzFjZaMPV61rQ6VK+f8eGxGaHEWPB+JRWW0AJnjf/Zgd2DgVwZlsNRI0wbWX0wHgYpyxSpvO+edCD/eMhLKk1V/VeyUwYDbMySkRERAVQvb9lERXA+wM+tDqNWN1kyx4/Aij7RQHAZTNgeXoPYrnuGw2kw2ihp+keTRAEPHLNqbjylGasa3XCoNWgxTE1jC6tUyqlR3/fAeCJ94fx1mEPAjEJnS4Lmu2G48JoIplCrzeCU1scWO6y4LUDk3hvwIcVDdXbogscCaPHBnwiIiIiNXCaLpFKZFnGjqEA1rc54TDq8MT2oezU1rGg0pLbYDXAYdKh0WYo232jwSKE0aNdf/ZifGyl+7jjVk5pcQAA3unz4uRmO2RZBgA83z2K9W1O/M3aZpyx2Il3+n3YPxGaMiH2sCeCZEpGR50FkUQKv3p3AABw6aqGBfzKSg8HGBEREVEhsTJKpJKxYBwToThWNtiwstGGmJTC7X/cg2F/FKPp6a1umx4A0OW2lm1l9EibbuGn6U7HatCi0318xbLWrEdHnRnv9PsAAHc88wEue3AzhvwxfHRFAy5cXg+rQYtzltTi4EQYn3nkHbx1aBIAcGBc+bfoqDPj9FYnAGBxjQmnpQNutTJn94wyjBIREZH6WBklytEbByfhiybw0RVK9Wz3sDJFd0WjDasabej3RvDzLX14/cAkFjmNMGo12Wpip9uCVw9MIJpIwqgrTqjL1UQoAQCoMeuLvJLjrWt14uldwxj2R/F89xi0GgFWg4gNy+qyz/nsaYvQVmPCPS/24Obf7sSPPnUyDkyEoRGAtlozmuxGWPQiPn1qc/Yc02pl4jRdIiIiKiDVKqOjo6P49re/jbPOOgsnn3wyPvaxj2HLli1qXZ5oQTy9axi/2No3p+f+xxuHsemVg9k/7xkJQBSATpcFokbA9We34fEvnI6zl9Ri/3gYDTZDNtx0uqxIyUDPePlVRzMTa48+A7RUrGt1IJJI4V9e6EFKBh655jT87otnHjds6ZwltXjs2nUw6TR4uWcCPWMhtDhNMGg1sBm1ePams3DlKc1F+ipKx5EBRpymS0REROpTpTLq9/vx2c9+FuvWrcODDz6Impoa9Pf3o66ubvYXE5WQ3+8cwaGJMD53essJq2JxKYW9o0FIKRm9ngjuf+0g9k+E0VFvmVLpbHYYcffHV+KdPu+UqaxdDUcm6q5ushfuCyqAIV8ULqv+uD2bpWB9Ww1anUa8dmASq5tsWJYeFjUdo07EqS0OvH5gApPhBD628sj+UFOZVasLxZzeMxrhnlEiIiIqAFXC6E9/+lO4XC7cfffd2cdaW1vVuDTRghoPxuCJJDARiqPeqlT+tg/6kUimsC69lxAA9o4pQRQAHt7ciz/vHQcAfGJ147TXPfq1gFJVtBm0U45/KRdD/iia7cZiL2NaVoMWD199Kn706kF8pMs16/PPXFyDNw56AACXrHAXenllRydqIGoEtukSERFRQagSRv/85z/j/PPPx9e//nVs3rwZbrcbV155Ja655pqq33NF5WU8pEy93TceyobRO575AIO+KDYur8c3PtSBRrsRu4YC2dc83z0Gu1GLs9tr8PGTpw+jxxIEAe21JvR6Iup/EQU26ItizaLSHezjMOlw+0Wdc3rumW3KhwRNdgPWLiqvCvVCMetEhlEiIiIqCFXCaF9fHx577DF84QtfwI033og9e/bgu9/9LgDgc5/73AlfK4oCnE5zTu8ripqcX0uUkbmPgjEJkfTeuL5AHB91mjERimPQF8Vpi514/eAk3u7z4vmvX4B9kxG4bQaE4hJCsSTOXVqHH12zbl7vW2czYiwYK6t7WEqmMBKMY4nbWlbrnsk6uwknNdrwibXNqK2ZuaV3rirxZ5LFoIUk5P5zmnJTifcSFQfvJVID7yMqFFXCqCzLWL16NW699VYAwMqVK3H48GH88pe/nDWMJpMyvN5wTu/rdJpzfi1RRuY+OjR55F7a3uuB1xvGWweVoz9uPGsxEskUbv7tTrzZPYptvR6sbLBiPBTHzqEAVrqt874XTaIATzBWVvfwkD+KZEpGrV4sq3WfyC+uORUAVPl6KvFnkkEU4AvGK+7rKnWVeC9RcfBeIjXwPqJ8uVy2aR9XZQKJy+XC0qVLpzzW0dGBoaEhNS5PtCAm0i26Fr2YnXK7ezgAAcBJDVasalTaON/t96LXE8HKRhuW1inVtDXN82/xtBu18KfP7CwXgz5lkm6zozT3jJL67EYdXt4/jrtf6Cn2UoiIiKjCqBJGTzvtNBw8eHDKY4cOHUJzM49GoPIxHlTC6Pq2GhycCOPRLX3YPuhHe60ZFr0WNqMWDTYD/rh7FACwqtGGM9ucWOQwYkV6Ou582I1aBGNJJNODkMoBw2j1+f8u6cTaRQ78bvsQZLl87lUiIiIqfaqE0b/927/F+++/j/vvvx+HDx/Gs88+i1/84he45ppr1Lg80YIYS1dGv3r+Epy7pBabXj2Itw55sLLxSNBc7rJgIhSHAGBlow0XneTGk9efOeU4l7myGXUAgGAZVUeH/FEIABpK8IxRKoy2WjPOXVILKSUjxCNeiIiISEWqhNE1a9bgxz/+MZ599llcdtlluPfee3HLLbfg6quvVuPyRAtiPBiHQatBq9OIf/3kavzbJ1fh5CY7LjrpyJEfS+uVttz2WjOshvy2XNvTr/dHyyeMDvpjcNsMU85MpcrnNCn3qjeSKPJKiIiIqJKoMsAIAD70oQ/hQx/6kFqXI1pw46EY6iz67HFE53XU4byOuinPWZ4Ooyubpt+EPR92YzqMllFldNAXRbOdVdFq4zQpVXxvJIEWp6nIqyEiIqJKwfIGUdp4KI56i/6Ez+lyKy27a1QMo4Fo+VSbhnxRNHG/aNWpOSqMUuV6df8EPv3ztzHsjxZ7KUREVCVUq4wSlbvxYBzLXCc+a7K9zoz7rjwZpyxy5P1+NmN5telKyRRGgzE02RlGq40jHUY9YYbRSrXlsAe3PbULSRnYORRAI/9/TkREC4CVUSIAUkrGcCA2p8E8ZyyuUWXPpD09wKhcwuhIMIaUzEm61ajGzMpoJQnGJGw+5Jny2PPdYzBolUFsmanZREREhcYwSgTgwHgIMSmFFQ35t9/OVWaAUaBM9oxmj3VhxaTqmHUidKLAMFoBookkbnliJ7722x3YPRzIPt49GsSqJhscRi0GGEaJiGiBMIwSAdlfylY2LlwY1Ws1MGg1ZVMZHfLFAABNDg4wqjaCIMBp0jGMVoCfvdWLHYN+CABeOzABQGnB3z8eQqfLikVOEyujRES0YBhGqSolkilIKTn7590jAdgMWrQ6F7bqZzdq4S+TAUaD/ig0AtBgZRitRkoYLY8PTmhmu4cDWNFow+omO147MAkAOOSJIJ6U0dVgQbPdiAFfpMirJCKiasEwSlXplid24vvP783+efdwECsbrdljXRaKEkbL4xf8QV8UDTYDtDxjtCo5TToOMKoA/d4IWp1GnNdRiz0jQYyH4tg7GgSgTAtf5DRiyB9D8qgP64iIiAqFv1VS1YlLKWzr9+G9AT8AZQ9Vz3hoQVt0M+wGbdnsGT3siXB4URVzmnTwFaiK7wnHceOv3sN7/b6CXJ8UcSmF4UAMrU4Tzl9aCwD4464RdI8GYdBqsLjGjGaHEVJKxlgwVuTVEhFRNeDRLlR1DkyEIKVk9HkiiCaSODjkRzIlY+UCDi/KsBl1GCqDM/2CMQkfjATwhfWLi70UKpKaAu4Z/cFL+7FtwI+fb+nDBRMhvNPnwxVrm7Cu1VmQ96tWA74oUjLQWmPCcpcV53XU4uHNvdBqBKxstEGrEbAo/YHTgC/K412IiKjgWBmlqrNnRGlJkwEcmgxj+4BSjSlKZbRM2nTf7vUiJQPr2xgOqpXTpIM/KkFKplS97usHJvGnD8bQZDfgjYOT+H8v7scLe8fwlV9vx6GJsKrvVe36vMpe0MU1JgDAzRcsQSSRhE7U4I6LOgHgSBj1lv6HZEREVP4YRqnqfDASRGZn6P7xMHYM+FFv0cM9hzNG1VZr1mMyHIeUTOHgRLhk92lt6fXCqNXg5CZ7sZdCReIwKWeN+lT88CQUl/D9P+/DkjozfvypNRAEwKIX8ctr10HUCHj8vUHV3ouAPo8SRlucShjtqLPgvivX4KGrT0FrOqA22o2os+jx5I5hyHJp/jwiIqLKwTBKVeeD0SBOWWSHThSwfzyEHQO+olRFAWC5y4JEUsbrBz34zCNv49k9I0VZx2w2H/bgtFYHdBxeVLVqzEoY9ajYqnv/a4cwGojhHy7qRGuNCd/auAzfu3QFltZbcNFJbjy9axjBMtlTXQ76vBHYjVo40x8sAMC6VieajmrH1WoEfOXcduwY8uPRrf38/hMRUUHxN0uqKlIyhZ6xIFY12dFea8b2QT8OjIewqkhhtNNtAQD897YBpGRg51BgllcsvGgiiV5PBGuaWRWtZk6TMmLAp1IY3T7ox+PbBnHlKc3Ze+vKU5qxvr0GAPDJkxsRSaSw+bBHlfcjoNcTQWu6Knoil65qwNpmO3706kF88b/eY4WUiIgKhmGUqsr+iTDiSRknua1YWm/B+4PKRN2VjdairGdxjRkGrQZbe70AkD1ioZQM+5Wpmk0cZlLVakx6AFBtiNGPXjkAl1WPr5zfPu3fd7mtEAAcGOe+UTXIsox9YyG015lnfa6oEfDAp9fi7y5YgoMT4ew+eyIiIrUxjFJV6U7/UnVSgxVfOLMVl65qwDlL67Cm2VGU9Wg1ApbWW7J/3jcWKrl9o8MBZZAJw2h1y1RG8zlrVEqm8Pi2QYwGYnh/0I+Pr26ERT/9UHejTsQipxEHJkI5vx8dcWAiDG8kgdNa5vazTqsR8NerGyEKwMv7Jwq8OiIiqlYMo1RV9owEYNGLaK0xYWm9Bf94SRce+cIZMOvFoq1puUsJo4trTIhKqeyQkVIxlK2MLvyAJyodmQFG+VRGN/d68f9e7MG3/7AbKRk4K92SO5OOOgv2c6KuKt5Nn+E61zAKKBOU1y5y4JUehlEiIioMhlGqKh+MBtHptkIjCLM/eYF0upQW4U+d0gwA6C6xVt1hfxSiANRbGUarmU7UwGoQ8wqjmTb0nUMBWA0iVs0ynXlJnRm9nggSKh8nU43e7fPBbdVnj26Zqw3L6tAzHsKAr7Q+JCMiosrAMEoV75ndI/jVuwOQUsqeqRUNxdkfOpOPrnDjlg0duGJNE3SigEe29uHFfePFXlbWcCAGl9UAraZ0AjwVh9OkyzOMhiCmb6MzFtfMek911JmRTMn43C/exXf/Z2/O71vtZFnGu/1enNbqhDDPD+IuWFoHAHiZ1VEiIiqA6TfrEFWQ/3y7H95IAqe3OhGTUjipxMKozajF505vAQDccHYb/nvbIO5+oQcbl9cXeWWKIX+MLboEIP8wum8siPM66lBr0eHik9yzPn9pndLCfmAiDA50nb8dg368ddiDda0OTIYTWN/mnPc1WpwmLK0345X9E7h6XUsBVklERNWMYZQqWkxK4cB4CEkZ2NKrHBGxwl2cY1zm4rr1i2HSifjBS/sxFlQqksU24o9izaLiDHii0uI06TAaiOX02kj6iKCLT3LjhnPa5vSatloTtBoBUkrGoD8KWZbnXdmrVocmw7jliZ0IxCQ0O4yw6EV8uNOV07U2LK3DI1v64IsksnuHiYiI1MA2XapoPekgCgDP7B6FSadBa83s5+wVU6aNuBSOU0imZIwE46yMEoD8KqM9YyHIOHK27lwYdSL+49Nr8b/OaUNMSuGD0SBuf3oPfvv+YMlNnS41P3hpP0SNgCW1Zgz6orj4JDdMutwGtV2wrB5JGXj94KTKqyQiomrHMEoV7ehhQN2jQXS5rRBLfO+jMmAJ2DMcKOo6tvX78Pn/fBfJlIxGHutCAGpMOviiEuQcemb3jStHtCx3za9Nfk2zHV1u5TW/fLsf/9M9hrv+3IPfvj807zWUE18koQT4HL7XiWQK2/p9uPgkF779kWWw6EV86pSmnNeyosGKeoue+0aJiEh1DKNU0bpHgrAZtLCkj245qaF0W3QzTDoR7bVmfFDkqbpvHZrEvrEQNAJwkru09tlScThNOsSkFCKJ+U+37fNEoBcFNOZQZW9OT4B97cAkas06tDiN2HLYM+/rlINoIoloIolrH96Kzz76Dq577D1EEsl5XWP3cAAxKYXTWp1Y1+rES187Z94fAhxNIwi4YGkd3jw0iZjEycZERKQehlGqaB+MBtHltqAjPQilXELVikYbdg8HcqqKqGU4EEOjzYA3vn4+VjaWfoinwnOm9wsO+aOQ5tkmO+iLosluzOlYpaZ0ZT4UT6LLbcW6Fie2DfiQqoCpRt5wAp5wHIAy4Omi+9/Ex/5jM3YP+fHx1Q3YNRyYd/DOnima3uutxj7bC5bVIZJIYWtvZX4IQERExcEwShVLSqbQMxZEl9uGpfVmACi5Sboz6XRZMBlOwBeRiraGzBTdUm9rpoXjNCth9DOPvIN7XuyBN5zAS3M8hmjQF81WOOfLrBezQbjLbcVprQ74oxJ6xkI5Xa+U3PrULtz43+8jGJPwnT/sgVmvxaomG771V534+48sh1kn4o2DHkQSyTnvk323z4eOOnP230sNZ7Q6YTdqcdtTu3H/64dUuy4REVU3TtOlinVwMox4UsZJDVbYDFr0+6JorzUXe1lz0mBTWhlHgzFVf6Gcj2F/FKdwii4dxXnUJNWnd41g0BfFm4c8eP4rZ0/5u+kM+qNY1ZR7hb3ZYYQ3ksBJDVasSlfqX9o3jmUuS07V1lLQ64lg+6AfAPCFX25DnzeCH39qDU5f7ITTaYbXG8aZbU78pWccL+0bR0qWcd7SOvz1qgasa53+mJZwPIn3Bnz4+OpGVdeq12rwk8+sxb1/OYBfbO3D509vgdXAXyGIiCg/rIxSxcoMLzrJbcW5HbW4/8o1ZVPlyxzpMhaMF+X9pZSM0QDPF6WpatMfjGgE5dikNw8pLZvD/ugJXxeISvBHJSzKsTIKAM3pe7HLbUWj3YgVDVb89K1efPXX22dtZ58MxxGOz2/fZaFMhOJ4eHMv3u714g87hyEAWFxjwmFPBNef1YbTF08NmecsqcVkOAEpJeOs9hq80jOBLz++HXtn2FP+4r4xRKUULjopt2NcTqSjzoLrz25DIinjfz4YxR93jSDOPaRERJQHfqxJFeuDkWBZHOUyHbdVD0CpjBbDeDCGpAw0cIouHWWRw4g7P9aF85bU4ZYndmD3cABJWWnpPtFwsEGfElZzbdMFgBUNyj7qzDXuv2oNfrG1Hz97qxdvHvLgnCW1074uJcu47pfbcEZbDf7hos6c318tT+4YwgOvH87++czFTtx8wRK8sn8CXzxr8XHP37CsDr95bxB/d0EH1rfXwB9N4IqfbcW9Lx/Ajz91cnY/aDiexFuHPXhy+zBanUasabYXZP2rm2xwW/W4+4UeJGVgwBfBjee0F+S9iIio8rEyShVDluUpA026R4NY7ir9o1ymU2/RQwAwVqQwOuxX3peVUTqaIAj46IoG2IxafP+vV+KBq9YCUIZdnciAP/8w+rkzWvDr687ItuRa9Fp86azFcFv1+Pnm3uOeP+yP4v88vQdvHvJg0B/DrqHiHpWUsXMogMU1JtzziVW4et0ifOW8dpzUYMON57RP+7Oq1qzHL69dh/XtNQAAu1GHL53dhq29XuxOn0WcTMn4ztO78e3f78b7g35cuqpBlaFF09EIAj7S5UJSBtprTXh0a3/2wwYiIqL5YmWUKsa3ntqNd/u9WNvswCmL7Ng7GsJlqxqKvaycaEUNasw6jBapTXcooPxy2WRjZZSm12AzwG3Vw6jVzNqmm62M5lFp1wgC9NqpAUsnavDZdS344csH0O+NoMV5pAvijUMePN89hq29XgDAockwpGQKWnFhP4MNxiTc82IPrjp1EVY0WLFzKIDzO2qxYVkdNiyry+maH15ej399aT92DvqxqtGG+147iDcOenDj2W2wGETV94se68vntuOvulyot+jx8Z9swTO7R3D92W0FfU8iIqpMDKNUMfaMBOAw6jDgi+D1g5MAkNfAlGJzWw1Fr4zmciYkVQ9BUM4NzdwvMxn0RWHRi7Ab1f9PzrlLavHDlw/g7V7vlDDa54kAALyRBESNACkl45AngmX1FtXXMJ3JcBxbDnvxwt4x/KVnAn3eKP7po13wRhJYnefPJZdVjzqLHntGg3h2zwge3dqPv1nbhBvOWZhAaNSJWN1kz66lzxtZkPclIqLKwzBKFSEly5gMxXHtma34ynlL4AnHcWAijLVlPA3WZdXP2v5YKEP+KJwmHYw6sSjvT+Wj0W7E0CyV0dFADI12Q0FaR9trTaiz6PF2nxeXr2nKPt7ricBu1CIYk/CJ1Y14YvsQ9o+FFiyM3vfqITy1cxiAss9y+6AfP9/cl/5zfvs5BUHAigYrdgz68fqBSaxptuPWC5fmveZctNaY0Odhmy4REeWGe0apIvgjEpKysr8KAGrMbzFPYwAAIABJREFUeqxrdUJbhvtFM9w2A0aLFEaH/ZykS3PTaDNgZJb71BdNzHr0S64EQcC6Fgfe6fNNmarb54ngtBYHfvvFM/CND3VA1AjoGV+Yc0mlZAp/6RnH+R212PQ3q3H/lWvgsurx1M5hGLUadKgQiFc0WNHricAbSeCL6xdDt8DtxxktDhMGfKyMEhFRbhhGqSKMh5W9lXUWfZFXoh6XVQ9fVEKsCEcnDPtjaOQkXZqDJrsRk+EEoomZj07xRSQ4jIU7L3fdYifGQ3E8uWMYUjKFZEpGvy+CxTUmtDhNMOpEtNeaZg2jmw958joCps8TQTAmYWufF76ohE+c3Iiz2mth1Il46LOn4JYNHbj9ok5VPiRbkZ5eXGfRZ4cbFUOLU/n3D8Wloq2BiIjKF8MoVYTJkBJGM+cgVoIjZ40ubHVUlmUM+aOsjNKcZPYVn6g66osm4DAVblfIBR21aLIb8M/P78NDm3sxHIgikZTRetQe0pPcVrw/4MegL4rv/c9ejB/z/6sDEyF87bc78ONXD+a0ht3DAVzx0FZc+KM38J0/7IFFL+Ks9iPHzTTajfjc6S24ZIU7ty/yGCsabRAAfGyFu6gdIJmjs/q9bNUlIqL5K0gYfeCBB9DV1YU777yzEJcnOs5kOAGgsiqjDdbZf8kvBF9EQlRKocHGMEqzy9wnM+1vlmUZvqgEewEro/VWA373pTOxttmOV/dPZocXHX3G8JWnLkIgJuG6x7bhyR3DeHbP6JRrvNwzAUA5B/TYoDoXf/pgFDpRwI1nt+H0ViduOLsNBm3hPu+tt+jxk8+sxY0LNLRoJi0O5Xs8wCFGRESUA9X/S/nee+/h8ccfR1dXl9qXJprRRLoyWmeunDDaXmcGAOxfoH1uGdljXdimS3OQuU9mOt4lFE8imZLhKMAk3aOJGgHr22vQPRrEzvSZoouPCqOrGm04Z0kNJsMJCAC2pI98yXhl/wSaHUYkUzL+693BGd9n0BfFbU/tyv7MAZQBai/sHcdZbTW44Zw23HP5Klxzeou6X+A01i5yFH3I2CKn8u/fx8ooERHlQNUwGggE8K1vfQvf+9734HCU7xRTKj+T4Tj0ogCroXKmv7qtetSaddg9HFjQ980c08E2XZoLt1UPjYAZj3fxRpSuBUeBBhgd7czFTsgA/uvdAThNOtQf0ylx28Zl+F/ntOGKtU3Y1u9DTErhtQMTuO2pXdg5FMDHVzdgTbMd2/p9M77H73cO4y89E3jwjcPZx3YM+jESiOEjXa5CfWkly2rQosakQz8ro0RElANVP6q+4447cPHFF+Pss8/GfffdN6fX/P/s3Xd4m+W5+PHvqy1Ztoa3He+RxM5yEpIQSAIBwoayR0sLtKUL2p62v9PTU8qBU2hLT1sKbTkHWlpWWSXsGQIkIYTs7cR7b9mSbFmSrfX+/pCsxMQQx9vm+VwX10Vk6dUjRVbe+73v576VSgmz2TCi51MqFSN+rDCzuPwhEoxaLJZT71I5lT9HCzPMlNs8E7o+pz/cMGl2hgXzDMo0T4Sp/FkaT0mxOrr6A0O+9obecDCaHm8c9/dmZayOGI2Snr4A/3PV/BO+D8xmA/Oy43m/rIP1B1rZ3tTDve9U0BcIolZKXLEkA3dAZv2+ZuLi9CiG2Iv5UU14hvGrh9tYkZ/Aitx4HthSi1mv5tLFGcSOUQZ4On2WchNjaOrpnzbr/aKZTp8lYeoSnyNhvIxZMPrCCy/Q0NDAb3/721N6XDAo43R6RvScZrNhxI8VZpZWpxeLXj2iz8NU/hwVWA1sKrfR3NFDjGZixgLXtrvQqxXQ78cpOmSekqn8WRpPSUYNjZ3uIV97ky2c2VeFghPy3lxSnIzHF2RNlvkzn2+OVYdaKfGT9QfRKCX+edMSZpl1qJQKMuO0eHxBSuvtg/acQrhbbkVHL7csz+DtIx38+0uHoj/73eVFBPt8OPt8n366EZlOn6Vsi54NZTYcDve4zJIVRmc6fZaEqUt8joTRSkyMHfL2MTm7ramp4Q9/+AP//Oc/0WhEJkWYeF1uHykzsOFOUUosMlDW3suSDPOEPGdrTx8psTpxUikMW0qsltLPKCfv6Qtf0BjP0S7H+8na/JPeJ0aj4n+vWcCOegezk4zR/dkABUlGACptvScEo5urw02OvjQ/ldtWZlPe7mJngxOTXs2a/IQxfBXTS15CDK7+Vjp6fSTHavEFQvzuwyrOzI1ndV78ZC9PEARBmMLGJBjdv38/DoeDSy+9NHpbMBhk165dPPfcc+zfv18EqcK4snv8FKcMfcVlOitKCZ8YH53AYDQ8Y3TmBfbC+EmJ0/FBZSchWUbxqYsY3dE9oxOT2R+uhekmFqaf2NsgL96AQoIKm5u1hYP3gB5o7ibDrCPNFG7aU5waR3Fq3ISsdyrLTwiXQ2+p7qK0zUW7q5/dDU4Ot7pEMCoIgiB8rjE5Ozj33HOZN2/eoNt+9rOfkZ2dzbe+9S3U6pkz+1GYWursHr79wkG63D6sM2isywCLQYNRq6S1e+I6VdrcPmYnGyfs+YTpLyVOSyAkY3f7SDAOvpDR3RcORmMnKDM6Wjq1kkyLntI2F/5gCLXyWJ+/0jbXhF0Umk7yEsKZ5T9tqaHPH0KllFiQFsfBlh5quzzkxIt9ZoIgCMLQxqSbblxcHIWFhYP+MxgMmEwmCgsLRbmfMG52NTjpcvs4LdPMqlzryR8wDSUatXSMYO7hSMiyjNPrxzIBnU+FmWOg83LrEB11u70BYrUqVEM0A5qqilJi2V7n4KJHduCKlBl3uPqx9fpmZAXGaMXp1CQZNXj9IS4uTmbL98/k/suKUEjh7sMhWZ7sJQqCIAhT1PhN5BaECVBp6yVOp+IvV89n3gwtl0syarD1jk1TlJPp7Q/PhDSLYFQ4BSmxkVmjriGC0T7/lCvRPZmfnJ3P91fn4PT62dUYnkc6sCd2XqoIRoeSnxgu1b1iQSoqhURCjIbTs608vbuJKx/bhT8YmuQVCoIgCFPRuAWjTz31FHfdddd4HV6YATy+IB9U2EZ1jIoONwWJMTM6+z6RmdGBmZAiGBVOxcAe47aeE8vJu70B4qZJie6AWJ2KGxanE6NRsqPOAcDhVhcqhURBoihhH8q62Umsm53I/OOC9XsvnsOtyzNo7u7jaHvvJK5OEARBmKpEZlSYNO+WdfDT149S1xVuFf6Xj2q5f2Ml3V4/v36vkt7+zx8rEgzJVHW6KZzhJ4dJRg1dbh+B0PiXukWDUcP0Ch6EyWXUqjBqlbQNVabb58c0RrM3J5JKqWBphpntdXb8wRAf1XRRmGREqxL/bA7l4uJk7rtk7qALg0atiusWpwOwv6l7spYmCIIgTGHiX1Vh0nRGSk+rOt0AvF9h4/2KTrbW2HnpYCs7G5wnPKbe7okGTI0OL/2BEIVJMSfcbyZJitUSksHuHv9SXZEZFUYqJVZH6xCZUYfHj2mafp6WZ1to6ennP984Sm2Xh1uXZ072kqYdq0FDlkXPvubPDkZfP9zG0fahRwMJgiAIM5sIRoVJY/eEg6vqTjdef5AmZx8Or59dDeGyuNou96D7Nzq8XPOP3Zz38Cc8s6eJClu47Guml80lRrqT2iagVNcRDUanXyZLmFwpcdoT9ozaevtpc/VHR39MN6tyrZj1ajZVdXFxcTJr8sWYkpEomWXiQHMPIVnm7nfKuW9DRfRnzd1efvluBX/dVj+JKxQEQRAmizjjFCaN3RMOfGq6PNR0eRgoQt1UFR4sX9PpGXT/0jYXMqBXK9hZ7yQ/MQaVQiJ3ho8NSDKGR9Z09PooHufn6haZUWGEUmK1HGjuGXTb9sh+yxXZlslY0qilxOl49zsr6HD1nzCyRhi+klkmXjnURmWHmw8qbHj9IU7PsbK2IIGXD7YhA/ube7D19rOrwcmFc5NmdB8AQRAE4RgRjAqTxnFcZrTKdqy5hdsXBKDWPjgYrejoRa2UOCPHSmmbi5AskxNvGDQHcCaayMyo0+tHo5QwqJXj/lzCzJIap8PVH6C3P4BRG/6nZUe9A6tBTUHi9MyMAigkiZQ43WQvY1ormWUC4NXDbXj9ITRKifs3VlKUbOS1Q23EaJS4+gP84KXDVNrchGSZS4pTJnnVgiAIwkSY2WfxwpTWFcmMNjq9HG3vRadSRBud6NUK6uyeQU17Kmy95MXHUJhkpLWnn9I2F4XT+CR3uCwGNSqFRMcEjHdxev2Y9WqRlRBO2UBH3fZIqW5IltlR72R5lgWF+Dx9oaXG6UiO1fJGaRsAP19XSHdfgOuf2EN3n5//OLcAgEqbG5VC4ncfVHP/xkqanN7JXLYgCIIwAUQwKkwah8eP1aAmJMOHlZ3kJcSQF9lbtjovHn9Qjp6MyLJMRYebwqQYcuPD9+npC8z4/aIQzswkxGgmZs/oNG42I0yu5NiB8S7hz2ldpNnYaZnmyVyWMEUsSo/D6w+hVSlYNyeJW5dn4PYF+em5BVwwN4m0yMWMB64opiAxhpcPtvLMnuZJXrUgCIIw3kSZrjApfIEQrv4AFxcn82ZpO3aPn1V58WiUCvY2dbNuThLvltnYVmsnIUaD1x/E4fVTmGgkL+HYHtGZ3kl3QKpJR6Ojjyqbm7veLsNqUKNTKcmy6rl8fiqZFv2YPI/TGxD7RYURSY2Usra5wh11q2zhBmSFSTP/gpFwcotnmXi3zEZBZK//N0/P4tJ5KdHPzdWL0mjr6WdFtpUV2VbuWH+IvU1OqjrdHGlzcdk8UbYrCIIwE4lgVJgUA11bF6TFcePidMrae1mWZaanL4DFoOa0TDN6tYIHNtXwwKYarJG5l4VJRtJMOnQqBX2B0BciMwowN9nI+gOtbCjvoLrTjTo5lk63j621djZVdbH+1tPG5Hm6+/ykxn0x3lNhbMXHaFAqJFojmdHqTjdKCXKsM7vBmDA8iyL7RudELk5IkhQNRAFuOi1j0P2XzDLxl6110bE6KoXERUXJE7dgQRAEYUKIYFSYFANjXeINagqTjNHsSUrcsUzKK99YRmmri6pONxUdbvoCQeYmG1FIEjnxBrrcvi9MFq84JZZn9jTz2uF2ChKNPPHlEgCe2tXIQ1tqsXt8WA2aYR+vweEdMps6sGdUEE6VUiGRHKvlUEsPdo+Pqk4PmRYDGpXYDSKEL0rcuCSdC+cmDev+SzLC5d21XR6MWiW/2VjJsiwLCTHD/54TBEEQpj4RjAqTYmCsi+VzAiirQcOqvHhW5Z042++W5Zl4Il13vwiKU2MB6HL7OKcgIXr7vNQ4AA63ulg9xPs0lEpbLzc+uZeHrppHvd3LznoH6WY9GWYdPX2iTFcYuZXZFl480MqXn9yLQoIFaabJXpIwRUiSxL+dlTfs+89NNqJXK5BleOjK+dz67H7ePdrBl5fOGsdVCoIgCBNNBKPCpLC7w5nRgfLbU3X2cQHZF0FanA6zXo3T62dhelz09rnJRpQSlLb2DDsYrbOHm0Ltbujm9cNtBEIyuxudeP0hAOKNIvMgjMxPzy1gTX48d6w/DEB+oijRFUZGpVRww+J0YjQq5qfFMTfZyBul7Rxq7aHD1c8Fc5O4tiR91M/T2x/g++sP8fUVWZyRax2DlQuCIAinQtRPCZPCEcmMxouSq2GRJInilHB2dGH6sWyTTq0kLyGGw62uYR+rrSfcYOado+04vH6+vzqHzXecwavfWMb9lxUNu4xOEIayItvKwrTwBZP8hC9GgzFhfHznzBy+uiy8l/TComSqOt18WNmJ3ePn4a11g0Z/jdSOegeHWl3c9XZZ9LvxeI9uq+PRbXWjfh5BEARhaCIYFSbc37c3sP5ACzqVAr1aOdnLmTYuKkpi3ezE6AiNAfNS4yhtcxGSh3diNjAHcmBuacksE5IkkWbSsbYgQfydCKN228osEo2aaBm5IIzW+XMSKUiM4c51hXz3zGzcviDlHb2jPu4ntQ5iNEoCQZlfvFU2KMBt7vby2PYG/vpJA8/ubR4yWBUEQRBGR5TpChNKlmWe3t1ESJZZmSNKok7FujlJrJtzYtayODWWlw62Umf3kBsfwz3vlPPO0Q60KgW58TH88cpijrb3khyrJdtqiM6BhHCZ9FiNhRGEAcuyLLz1rRWTvQxhBrEaNDzz1SUAdEa2eexpcFKcEktIllFI0pCP+6TOTpfbxyXFJ46GkWWZT+rsrMi2cFZ+Ar94q4y73ipjeZaZdJOeDeUdSJJEUbKRP3xYzQMfVvM/lxezJn94WyIEQRCEkxPBqDChOnp9uPoD/L+1+VxbkjbZy5kR5kWaGx1udZFp1vN+hY2ilFgyzDrePNLBjnon975bwao8K/dePJc2Vz85VgO1dg+LI1lRQRCE6SIhRkNOvIHdjU6uLUnjisd2cdvKLK5YkHrCff+0pZZ6u4fVefHE6Y71KHjrSDuvHGqjo9fHymwrF8xN4kibixf2NfNeuS16v/PnJHLnukL2NXfzl4/q+OW75cxJXnJChYogCIIwMiIYFSZUVacbEI1NxlK21UCMRklpq4u8hBi8/hDXlaRxVn4CG8ptrD/QgscfjGZE23r6OKcwkcUZJs4tTJzk1QuCIJy6pRlm3ihtY1eDk063j531zhOC0U63j0pb+N+cDWU2rl4UvgD6QYWNu98uJyVOS068gTPzwlU6Pzo7j++vyaXd1UdLdx/trn5W5ljRqZWcnm0lLU7HTU/v5RdvlfG/1yxAqRAX8oTx9drhNrbV2vn1JXPFhWNhxhLBqDChqiMnBqKxydhRRJobHW7tiZbclswyoVEpmJscy57GbiC8V9TrD9LdFyAlTsstyzMnc9mCIAgjtjrPyr/2t/Dg5hoAyjuONXGrs3v4r7fLo9+HJp2K1w63RYPRR7bVk58Yw99vWITuU3vkVQqJdJOedNOJ2xeyrAb+49wC/uvtch7bXs9tK7PH6dUJAjg9fh7YVE1vf5CKDjezk42TvSRBGBeigZEwoSo73SQZNYPKpYTRm5caS3Wnm+31DtJNOhKN4RKy+cc1kLH19tPSHW7AkRInSswEQZi+Tsu0kGTUUO8Ij6pqdPbR2x/A6fXzw5cOc6TNxTtHO7Do1dy2Mpuj7b28daQdfzBEvcMbzXieqouKkrm4KIm/fdLArgbHWL8sQYj62/Z6PL4gSoXEhvKOyV6OIIybSQ9Gyzt6ufWZfTywqXpM2rQLU1t1p5v8RJEVHWvFqXEEZdhe52DRrGOjXxZEZpKqlRJBGUrbwtmDlFjdpKxTEARhLCgVEhcVJQOwNCP8nVfe0csze5po7enjngtno1MpWJlr5aqFqZSkx3H/xip2NzoJhmRy40e+VeSn5xaQZdVz/8aqMXktM1G93cOL+1uGff/e/gDvHu0gKM4DAfD6g7x+uJ0L5yaxIsvCe+U25GF2zBeE6WbSg1GzXk0gJPPMnuYxadMuTF2BYIjaLo8o0R0HK7IsfPP0TC4qSuLaRccaQy1Kj0OvVnBBpAvvweYeQGRGBUGY/q5amMqi9Di+tyoHgEMtPbx8sI1VufFcVJTMs19bwo/PykOpkPj5ukI8/iB/394AQF78yP8d0quVXL0wjXqHlyand0xey0zz1O4m7n+/aljvT0+fn9tfPMSdb5XxQWXnBKxu6ttU1YnHH+TSeSmcNzuR1p7+E+aJi+BUmCkmPRhNjtVy94WzAWhweCZ5NcJ4qnd4CYRk8kQwOuY0KgW3rczmngvnUJQSG73datCw4Tunc8OSdAA+qukiRqMkySiCUUEQpreUOB1/vX4R81LjSDRqeGJXI06vn2sindpnmfXE6sKtMbKsBmaZdexv7kEhQZZ1dCOtlmdbANhR7yAQkkVg8Cn7msK9CrZUd0Vv21zVyY9ePsxfPqqlPxCK3v6HTTWUd/QSo1GyuUoEowBvlXaQGqelZJaJNfnxaJTSoC7Pf9/ewCWP7sDrD07iKgVhbEyJBkazTHoUEtTbxRXGmay6UzQvmgw6tTI6hsDu8XN6tkV0gRQEYUa5eVkGbx7pIDFGw7JM85D3WZZpocnZSrpJN6L9osfLsuhJidXy9O4mHthUgwQkxWq5pDiZqxamsq3WwXmzE1EqJEKyjC8QGvVzTnX+YAi1UkGn20dDZC/vu2U2ajo9XL0olUe21dPS3cdHNXaanF5uX52Dqy/A20fauWHxLFz9fj6o7Iwe54tIlmUe2VbP9noH3zw9E4UkYdSqWJljZWOFjR+elctLB1r534/rgHC108CFEUGYrqZEMKpRKUiN00W/vISZqarTjVIKjyIRJlasVoVercDrD1Fy3J5SQRCEmeDaknSuLUn/3PssyzLz0sFWckdRojtAkiSWZ1t49VAbRSmxLEqPo6y9l4e31vHywVZae/pRKiRW5li4/cVDeP1BnvnqEhSR8RybKjsJyTJrp/F4rd7+ADqVApVSQXl7L998fj/fPD2L1LhwT4JlmWZ2Njg50uZif3M39Q4vPzo7j2BI5sHNNWysCGdBYzRKbl6WwYGWHl473M6uBicrc6yT+dImzZ7Gbh7b3sAlxcmDOt6fNzuRTVVd3PbcAQ609LAi28KuBie7G50iGBWmvSkRjEK4ZKbeLsp0Z7Iqm5tMqwGN6ot5xXMySZJEcqyWOruXknQRjAqC8MWzNMOMUiFRMEZN9K5fHA5+f7A6l1idikAwxHdfPMShlh7MejUv7Gvm5YOt0b1+O+sdrMi2EpJl7n+/CofHx5+vVrP0MzK5U1V/IIRCgpue3kuMRsWj1y3k0U/q8fpDPLSllkyLHr1awY/OzuOed8Ijdt4tC5eYnlOQQKJRw7yUWBqcXrrcPuYmGzEb1CzPMpMQo+E3Gyv5+40lJMRoJvmVTrxttXZUCol/Pyd/UHZ4dV48Z+RYae72ctPSWXx3VQ63PXcgOrpNEKazKROMZloM7G3sRpZlMdh3hqrudFN83KgRYWIlx2pp6e4btKdUEAThi8KkV/PEjSXMsoxNN/H8hBjuXFcY/bNKqeChK+fR5fHxXpmNv2ytA+Dn5xXw8NY6XtjXwsJ0E7VdHjrdPrQqBT9/8yivfmPZtCnh3dPo5PvrD7Eyx0qTMzwq7OZn9lHb5eGW5RnYen3srHdw/pwk8hJiePIri3H7Anxcayc/IYakyJaRRbNMgzq/Q3hLyR+uKOa25w5w34YKHrhiHmXtLh7cXMNPzykgexQdkKeLHfUOFqbHof/U50GnVvLHK+cNum1Jhomndjfh8QUxaKbH50cQhjJlUlRZFj19gRAdvb7JXoowDty+AC09/WK/6CS6qCiZry3LEJlpQRC+sGYnG4nRjN91eJ1aSbpJz2XzU8i26vnJ2Xl8aUEql85L4aMaO2v/vI3ff1iNQoK7L5iN3eMf1JhmJGRZZmO5Dd9xTYHGy9tHOvAFZTZVdTE32cjdF8wmRqOkJD2Om5Zm8F8XzObNb63g58cF6TEaFQ9fs4D/umD2SY8/NzmWW1dksrXGzvN7m/n2CwfZ3djNh1+AxkZdbh8VNjfLs4ZXdrs000wwJLNpmO9NIBjiX/tbaO4eektcT5+fX75bziWP7qAq0uNDECbClDkrHehs9/iOBt4sbaetp2+SVySMperOcAm26KQ7eS4qSua2ldmTvQxBEIQZz2rQ8K9bTuO6SCnv11dkcs+Fs5mbbORgSw/zU+M4pzCB3HgDz+9rocs98gvxe5u6+dkbR3lub3P0toMtPVz66I4x6cVx9zvlfFjZSTAks7m6i5U5Fs4tTOAna/O5uDiZf9xYwqPXL4p2Lh7K3ORYZpmH18H46oVpxGiU/O7DahKNGhKNmi/E6L+dDQ6A4QejGWbmJhv505Za3L7A597X6w9yx/pD/Pb9Kv66rf6En/sCIe5Yf5g3S9vp8wf5wfpD2D0iOSRMjCkTjBYkGInRKHnxQCt3v1PO/3v1yGQvSRhDA1fZ8hNnfpmNIAiCIBzPoFFyUVEyv728mJx4A5fOS0aSJK4rSaO8o5cL/m87rx9uG9GxB8aovLC/hUAoPGLmkY/raHP188yeplGt2+7x8WZpO8/ubWZ/czdOr59Li1P49aVFLEgbn203sToV3z0zmxXZFh69biEL0uK+EMHoxvJOEmI0zE4yDuv+ysje0k63j2f2NH/ufZ/c2cjuxm5yrAY+rnXQ3O0dNHbnwc01HGlz8atLi3jwqvl09PrYWD7zs9HC1DBlglGzQc3G757Olu+fwdULU6npchMMjX5uly8Q4hdvlVHXdaw5kizLbK7qGpPjC8NTbXNjUCujXfYEQRAE4YsmIUbDCzcv5fL5qQBcNj+V311eTF6Cgad2N41oXunBlh7USol2Vz+bqzopbXOxs8GJSafizdJ2nKPIcFVEgsADzd08u6cZrUoxIZ1ury1J509XzcdiCAdnTc4+evsD7Gl0cs875dGge6r4oMLGU7saR/x4h8fHx7V2LpybdEqj1+alxnFappm3jrR/5menw9XPU7ubOLcwka+vyMTp9XPrM/v58SulHGrpYUNZBy/sb+HGJemsLUigKNlIcqyWvU3OEb8e4Yut0+07pRm4UyYYhfDmf71ayewkI76gTJtr9KW6lbZe3jnaMWi/wd6mbn7yainbau2jPr4wPFWdbvISDNG29oIgCILwRadSSKzJj+fLS2ZR2+VhV8OpBQAhWeZQaw8XFSWTbtLx3N5mHt/RQKxWxe+/VExfIMQrB1pGvL6KDnfkeWBzdRdXLEid8GY5hZFMYXlHL0/sbOSN0nbeH+U+27EiyzJ/3VbPT18/ykNbamkd4RazDWU2giGZi4qTT/mxFxUl0eTs42BLz5A/f/lgK/5giO+tCmeblVJ45rhWpeDOt8r45bsVLEiL445VOUC4+/7SDBN7GrsJjeDiyFT04v4W7t1QQSA4/vuqJ0NNl5v/fOPokOXaIVnmV+9VTNjFhd7+ADc8sYffbKwc9mOmVDA6ICsyh7LePvq9DrVlbQwXAAAgAElEQVSRcTHHj42psoW/XGu6xCiZiSDLMtWdbrFfVBAEQRCGsG5OEha9mj9tqeXxHQ1c/8RunB4/d755lO+8cICfvX6U/3m/in/tb4lWdQWCIbbV2untD1KSbuLakjT2N/ewqaqLa0vSWJhuIifewAdlHSNeV3lHL8mxWqwGNWqlxFeWzhqrlzxsA2WrO+od7IwE6//Y2TCpgVKjw8tdb5Xx3X8d5NFP6lmTFw/AxhEGye9XdlKQGDOiJo9nFySgVSl4vbR9yJ9vrbEzPzWOWWY9Jr2a03OsrMiy8J/nFdDS3ceKbAv3XzoX1XGjZJZkmHF6/dR0Tr/z5NcOt7HmoY9Z9/An1Nk9bKnu4v73q3j1UBv3vVc5ouqDqe5PW2p5r9zGW0dO/F3fVe/k5YNt3PN2OX2fylZ+XGuncQz2lR/v6d1NOL1+3q/oxNV3LDj2+D47UzplRrscb6CZUb3Dy8qc0R2rtiv8JtcdF9gOBKi1M3yu6btHOwjKMhcVnfqVtrHU6fbR3RcQnXQFQRAEYQhalYI7zy/kx6+UUhYpjf3Zm0fZ3eCkIDGGjl4fDo8fV3+AGI2SM3OtfOeFg1RELq4vTI/DrFfz6LZ6giGZ60vCjZNW5cbzzJ4mevsD6NRKmpxesq3D791QYetlTpKR1fnx9AdCJEdGs0ykhBgN+QkxPL6jERm4cUk6z+xp5rHtDXzz9KwJX0+d3cO3nj9AfyCESa/mu2dmc/OyDL72z31sKLNx/eL0QTNCT8bjC3KopYcbl4ws0I/RqLikOJlXDrZy7aK0aCYZwNbbT1lHL987Mzt62++/VIwsh/ecrs6Lx6g9MRQYmH27u9FJ/hjN5Z0Isizz1K5GEowa2l39/OWjWvY1dTMnyciKbAuP72wkNU47o5o5lrW72FpjRymFM8BXL0zlqV1N/Gt/C8mxWnRqBVqVgpaefv78US0/PjsPGXhgUw3P7W3GalDz6HULo4nAkXL7Ajz2SQMv7G+hMDGGCpub9ypsXLkglbaePm58ci+H7jl/yMdOyWDUolcTq1UNymaOVF3kGHV2T3SGad0Q2dKZ6Jm9zbj7A5MejB5rXjR9vtAEQRAEYSKtzovnp+fkU9vlodLWy+4GJ2lxWp78ymJUCglZlrn28d38c3f4RLPW7uHfzsolw6yPdqr96bn5yHK4D0f4mFae3NXItlo7nW4fD26u4Z83LRnWv8def5B6u5fzZidy2byUcX3tJ/M/lxfxjecOEKdV8YM1ufT0BXh0Wz258QbOKUyc0LW8sK8Fjy/IU19ZPGj26bo5STy4uYZVD27l91fM44xh7q3d2+QkEJJZnmUe8Zq+c0Y2H1R08h+vH+GqhWlctzgdlULi45rwdrQzc+Oj91VIEkR2TA0ViAKkxulIM+nY0+jk+khH6OngYEsPdXYvd64roKy9lxcPtKJUSNx94Wxy4w10uX389ZMG8hONrC1ImOzljonHtofL8r+5Mos/fFjNU7uaeHhrLUUpsVTa3Hj8Qa4rSQPg+X0teP1B9Golz+9r4fJ5KWyp7uKut8t54sslo1rHn7bU8vLBVlblxvPv5+Rzx/pD/O/WOmo63TQ4vPg+p0R6TILRRx55hA0bNlBbW4tGo2HRokX86Ec/orCw8OQPHoIkSWRZ9dSPQep4IPB0+4J0uX0kGLXUdp0YoM5ETo+Pdlc/vkBoUmdLDpRFizJdQRAEQfhsVy8KnzR+WNnJvuYj3LhkFqpIQxtJkri2JJ3fvl+FUoL7LytiTf7gE+oL5w6++DwvNQ6LQc0HlZ3Yen2EZHhmTxN3fWrmpyzLbKt1sCAtjlidClmWeWhzDTKwMM00fi94mGaZ9Tz31SX4QyEUksTP1xWyt6mb1w63TXgwuqvBweIM06BAFOCqhakY1Aoe2VbPq4faOCPHyv6mbnY3Ovn6ikwkSaLR4aXO7sGkVxOnU2HWqdlR70SrUrAwfeTvs0mv5r8vms2Dm2v54+YafMEQl89P4endTaTGaclLOPWs19IME5uqugjJ8rTo99HZ28//bavHoFZy3uwklmSYeeVQGzcuSY+ef/7neQVU2tzcv7GSxbNMmPXqSV716FR09LKpqovbVmZxxfwU3jjcxp8+qsWgVvK7LxVT0+nhoS01XL0ojSyLHq1KydO7GwnJ4c/rT8/JJ32njoe31uHw+LAYNKe8ht7+ANtq7bx8sJVrFqXxk7X5ANx1wWz+vr2Blw+24gvKfPuMz65iGJNgdOfOndx4443Mnz8//AX20EPccsstvPnmm5jNI7vSk2nRs6PeyZE2F8mxWuJjTv0N8gVCNDu9zE+N5VCrizq7F41Kgd3jJyVWS5urPxqgzkR2j5+gDA0O76RmJas73SQaNdP+l14QBEEQJsJZ+fH8+er5LM0YfA51cVEy22rtXFyUfEIgOhSlQuLKknQe31ZHSAajVsk7ZR18d1UOvkCIkCyTbtLx54/qeHJXI/NTY3n4mgVsqe7ixQOtfPW0WSwbRcZuLA1keyHc+GltQQLP7W2mtz/wmRm+sdbh6qfO7h0yU6xXK7lyYRo1XR5eOdTGm6Xt3PdeBf6gzFkFCTTYPdz1djn9gRMzRCuyLGhHmTRYkW1lRbaVn71+lEe31fPc3mbcviAPXTVvREmXJRlmXjvcTmWHm9nJwxs3M1n6/EFuenofTq+fH67JxaBRYtDoefnrp5F0XGm5SqngF+cX8tV/7uOJnY38YE3uJK56dGRZ5n8/riNGo+T6knR0aiUPX7OAu98pZ1WuFatBgzVTw5NfWRx9zB2rc7hsXjL7mrq5ZF5KpFnVQEl2N+fNPvULOz96pZR9Td0kGTV867jy5+KUWH7/pWKcXj/7m7o5M/ezKwXG5Lf3scceG/Tn3/72tyxdupS9e/eydu3aER0zPyGGt4508LV/7gPgF+sKuWx+Ck1OL7//sBpZhv+6rBiLKvwLFpJl3iuzodco0avDv9B2dzgYOys/IRKMelArw/c/K/IlVmf3zshgtM8fpC/yhVfT5Z7UYLSq0yOyooIgCIIwTJIksTzLcsLtBo2SB66Yd0rH+trpWTy+rQ4Z+MX5s/mP147w3N5mPqiwoVMr+d6qHJ7c1cjyLDM76538+aNa3L4gZr2a21flTNnqsbMLEnh6dxNba+xcMDdpQp5zd2O4gdKyzBP/bgacNzuR5/e1cPc75RQkxlBpc7OhrIOndjUxN9nID9bk4vYF6ekL4PD6ae/p55zCsSsZ/ek5+Ti8PqwGDddFGlmNxLEgxTnlg9E3StvpdPt4+Jr5nHbc303KEOMEC5OMLM8y82FlJ99fPXU/3yfz2PYGttbY+bezconVhcM5k1590u+HLKth0P7QuSmxxGiU7GpwnHIw6guEONzaw+XzUvi3s3OJ0ZwYVpr1as46SUn0uFxKcrvdhEIh4uJGPhD52pJ0ChON9AVC/PLdcvY3d3PZ/BQ+qrGzNVIDv7Gsg2vmhUtS9jQ6ufOtsiGPtTzbwt93NPB/H9dFr56dlR/Pc3ubeXhrHV89bdZJ36jpxuH1R/9/MrsGB0IytV1uTsucPnsOBEEQBGGmSDXpubg4mSNtvZydH8/qvHie2hUu1QN4elcjRm04yL3rrTLeKw8HqYvS46b0ifq81FgSjRr+ubuJVXnWIU+Ex1IgGOLtIx2YdCoKkj77Avv8tDgKE2NIjtVy3yVzueGJPTy9u4lASObO8wvJjR/fi/Nmg5r/u3bhqI+TFKsl06JnT6OTL09CF+Xh8vqDPLu3maKU2BMqCT7L6rx4flNbxaPb6tnd6GRRuomvnpYRDer6/EHKO3pZkDY1fwfq7B4e3VbPBXOTuGGUe3pVConFs0zsPsWxUgCVnW78QZmVOZZR/f6Ny2/ufffdx9y5cykpOflmWKVSwmweupb9/ITwlZgXDrTQ7OrHbDbQ2uvDrFejUkrUdrqjj205Gm5n/NhXl6BTh2dgeXxB/IEQywoSueeyYrZU2OjuC7BmdiJnFaeypqCFXfUOntrTzJdOyxyLlz5lNPQeC0Ybe/o/8z0eb9W2XnxBmQWZlklbw8kolYopuzZhehGfJWGsiM+SMFaUSgX3X72QoBzu2vuts/LYXN1FcqyWdlc/uxu7uWxBKonxRi6Yn8bGik7Az9dOz5ryn8H/vqyY25/bz5ef2ktxmonfX70geg441n74/H621zv4zwvnYLV8fkD5+u1noojs9T0jP4EX9jRRnBbH4ryJ3d86WkuzrWyusGEy6Qd9J4VCMoGQPKn9SAAON3fztcd30dMX4C83lGA5yd/LgEtKZvGbjVX8bXsDCUYNh1pdfFDVxSNfXkx+kpGfvHiQVw+08NeblnDWKPYky7LMqwdaKEgyUjyGe69f3FyDWqXg7suKsYxBdec5RSnc/cYRanv6KfmcrP+n1ZSFRxmdPjsJs0k/4ucf82D017/+NXv27OHZZ59FqTz5F0IwKON0fn7mLj1OywcVnTidHiraesi06FEpJGpsvdHHHm3uJlarYn6C4YSrGN3dXtZkmVlz3L4HV4+X311WxO8+qOKN0nYcDveUvPoxUo0dLiDcEr2ireek7/F42VfTBUCaQT1pazgZs9kwZdcmTC/isySMFfFZEsaK2Wyg19UHgBfIN2m5ZXkGy7Ms/Pq9SuodXs7IMuN0eliYHINCgpAMs+On/mdwaWos919axHP7mtlwpJ039zWNS0Mjty/AW4fbuGFxOlcUJZ3S+7Ig2cgLwLrChCn/fn5agVXPS24fRxscFGVZo+u/d0MFB5t7ePIrJeMW/A/Hczvq8QVC/O36hSxMix32+6slnFlvcvbx5JdLaOnu499fO8KXH9vBBXOTePVAC0qFxG/fKWNBomHEDZwOtfTw/9YfAuCrp80addl7nz/I20c7eGlfM5fNS0EZCI7JZ2ptroU/6tX8cWMFD145f9iP213bRXyMBl0oNKx1JCbGDnn7mF7S+NWvfsWbb77JE088QUZGxpgdN8tioLsvgNPrp87uJduqJ8uqpyYyMgTCY1qyrScGoieTbTXg9gWx9frGbL1Tgd0Tfj1LMkw0Orz4htgwPxEqO90oJU7oOicIgiAIwsSTJInvnpnDkgwz5xQmEKNRsiI73FzErFezMC0OnUrB7Gkyjm1Nfjx/vmo+Fr2ajeWdY3bc8o5e3jrSDkBZey8y4W1fI1nft8/I4vL5kzseZySKU8PBQ2mbK3pbb3+Ad452UGv38I+djZO1NAD2NXUzPy1uRPtif33JXP5x4yISjVoWppt45LqFKBUSz+xp5vRsC3edX0ilzc2Wqq4Rr2/9gRYMaiWXFifz5K4mnt7dNOJj9QdC/OClw/zqvUrS4nR8bdnYxVl6tZIbl6SzrdZBeWTO8cm09fRxoLmH4pTYUSfzxiwYvffee3njjTd44oknyMvLG6vDApBlDad+S1tddLl9ZFsNZFsNODx+nJ5wOWptJEg9VQPDn2tn2MxRZ2TP6OIMc7ijrnP0Y3JGotrmJsOiH3WXOEEQBEEQxtY3Ts/ixVtPw6A5lt36t7PzuOeiOaiU0+ffbaVC4uyCBD6u7aLPHxyTY/5xcw3/9XY5VZ1ujkSCseLkoTM7n0enVvL1FVnjvqd1POQnxKBRSpS2hl9/nz/IxnIb/YEQRSmxPLmzkQ5X/2c+/vXDbexr6h6Xtbn6AlTa3JSMsEFTSpwuOp8XwvHAq99Yxoe3r+Shq+azbk4SJp2KDypHdoHD6fXzXrmNC4uSuPP8Qs4uSODhrXV0usPJot0Nzs9870KyjNsXiP75UEsP337hAPuaurn7gtn865alpA7RnGk0rl6Yhlal4NVDbSe977ZaO1/6206au/tYkjH68uMx+aa55557eOmll/j9739PXFwcNpsNm82G2+0++YOHIcsSDhg3VYU/EFlWQ/S2eocHV1+ALrePnBFk3wYydvUzLBh1ePxoVQrmpYS/OI/PIk+kqk43+aKTriAIgiBMOWqlgoRPjc6bmxzL2mnY1HFtYQJef4hn9zaP6jgNDi9NTi97Ig1dHvukniNtLtJMukHjZb4I1EoFs5NiOdLWQ2u3l3X/+wm/eq+STIue+y6eQyAk83rp0MFLg8PLvRsqeHxnw7is7WBLDzKwaNbIm6V+mlqpiDY6VSkkzsy18nGtnUDwWHVhIBjioc01bCy3IcvyZx5rU2UnvqDMFfNTUUgS3zszm0BI5uWDrexv6ua7/zrIT14tJfSpY/iDIe548RBXPrYLW28/f9/ewDee209rTz+/vGgOFxcnj8u2wlidirPy43m3rGPI8UMfVXdR0+XG4wvym42VZFoMPH3T4lE3UIIx2jP6zDPPAHDzzTcPuv3222/njjvuGPXxU0061EqJLdXhVHm21UBkXzh1dk+0lvv4VsXDFW9QY9QqqZ3EjrPjwe71Y9aryYq8V5PRUdfjC9Lc3cclxcknv7MgCIIgCMIInZZp5tzCRB7eWkdyrJaLik793MPp8XP9E7vRKBXIwLmFCWys6MSgVrIy57PnJM5k89NieXF/C398v4o+f4iLipJYW5jILLOeZZlmXjnYxs3LMlEqjgVI3V4/f/uknpAM1Z3jc/65t6kbpUJifurYBaOftjo/gTePdLC/uYelmeG+M//Y2chTkXLbdbMTuWZRGo/taEAhwY/PzifTEs62bqnuIjVOS2Gk83KW1cDKHAvP723mlYOt6NQKjrb38vaRDi6OnCfLssxvNlays8GJSiFx3eN7cPUHOH9OIj87r2Dcs+uXFqfwbpmNP22p4cqFqWRaDKgUEjvrHfzolVLMejWFiTG09vTzt+sXMjtpbEb+jMmrKi8vH4vDfCaVQiLToqe6MzwnNM2kQwI0KgWP72wkThe+UpUzgmBUkiSyrQbqHJNTxjpenB4/Fr0arUrBLLN+UoLtmq5wNlZkRgVBEARBGE8KSeKXF82mvMPFe+W2EQWje5qc+IMy/mCQwsQY7rpgNl0eP/uauilKmdqzNsfLdSXprD/Qykv7mlmVa+XuC+dEf3bFglR+9sZRdjc4o/tp9zQ6+fYLBwGI1apod/XT2x+IZhzHQkiWeb/CRsks07g2UFqRZUGrUvDs3maWZJio6HDz2PYGzp+TSJbFwKOf1LOxwkZ8jIZur5/HdzRw1wWz6fMH2dng5EvzUwZlMW9dnsl/v1uBVqXgV5fM5YFNNTy8tZZzZyeiVSl4Ymcjrx1u5+srMkmO1fL7D6v593PyuXph6oQ0WT0ty8zaggSe39fC8/vC+12LUmOp7Ogl06Kn2+tnd6OTn59XMOL5tUOZNgXsd50/m+11DlJNWlSRqy93nJXH+0fbcfuCrM6LJ800svrpbKuBd4528J1/HeR3lxeN+MqDLMvsb+7B4w+yeJYJfeQXpNvrx6Sf2NIOu8eHJVJOkhtviAaGE6nKFglGp0kTBEEQBEEQpi+VUsH8tDh21p/6zESAXQ1ODGolD101D5NejV6t5I9XzOPF/S3TsgHRWEgz6bh5WQaPbKvnmpK0QT87M9eKVqXgo5quaDC6vc6BUiFx57oCNEoFP3+zjOpO95gGL/uaumnu7uO2lVljdsyhGDRKvnNGNn/cXMOfP6plW60Ds17N/1ubT5xOhd3j42BLDw9eOY+/bW/g9cNt3LE6h4MtPfQHQqzKix90vIXpJtbfelr0z989M5vvvXiIp3c34vD4eX5fC+fPSeRbK7OQJIlL56VEY56JoJAk7r+siCanl4MtPRxs6eFoey/z0+K4fVUOSkmipz/AgrSxzUZPm2C0KCWWopTBG8e/vSaP6xemjvrY1y9Ox9UXYHN1F6WtLpZlnXq3NIAj7b3c9vwBAL61MotvnJ7FrgYHd7x4iH/dchoZlmMbpTvdPr6//hC/umRutInSaAVCMuXtLgIhedAe2tx4Ax9Vd+ELhCZ0JlRVpxu9WjHiiwSCIAiCIAinYk5yLG8d6aCzt5+EU5zBuLvBScks06DAyaBR8tUx7Fw6Hd2yPJOzi1LIMw1+P3VqJUszzHxca+fHsowkSRxo6WF2kpFLilNo7QmPFKru8oxpMPpGaTsxGuWE7G2+cUk65R29PLkrXJr7hy8VRxNMPz23IHq/a0vSWH+glad3N3GwpQerQc3iWZ//mk/LNLN4lon/+7geCMcjP1h9bPzLRAaix5tl1jPLrB9RdcFITJtgdDzNTjLyH+fms7m6izq7Z8TBaOVx7ZDbIx2yDrW4CMpwtN01KBg90NxNpc3NrgbnmAWjbxxu4773KqN/TogJf2nkxscQlOGMB7cCYFArMWqVJBi1PHLtsQHR5R297KhznPCl6+oL8PbRdhSSxCXFydH7y7LM4VYX8yNXSEKyTCgko1IqkGWZ6k43ufExI57PJAiCIAiCcCrmRvaxHW3vZdUpBKMdrn7qHV6+tGD0SY6ZRqmQWJJlGXKW5Bm5Vj5+306Dw0uaSceRNhdXRt7DlFgtBrVyTJtoenxB3q+wsW5O0oTMOJUkif++aA7XL07H1tt/QrZzQG58THSEC8DPzitAfZKO1JIk8bPzCthQ1sGa/IQx24M53YhgNCI+RkOsVjWqRj+1dg86lYJUky46WmWgPPbTezYHnmcs93Iebe/FqFVy38VzcfUFWJYV3my9Oj+e21fl0B8IIsvg9gWps3v4pM5BVaebeZHN3+sPtPDywTYun58yqKz49dI2HthUA4SvEA5cKdlW6+CHLx/msRsWsSAtjr98VMfOegcPXFHMNY/vprc/+IUtaxEEQRAEYeIVJhlRSPCPHY28dLCVH6zO5Zm9TVy1MO1zT/YPR8a3lKSPX0OcmWhlTjiBs6mqiyUZJvoDIRZG3kNJkshLMFA9hsHoxgobXn+ISye4OWa4OvPzR/v8+zn51HR5CMkyl80b3vlvttXAbSuzR7/AaUwEoxHRRkajGPFS0+Uh22ogRqvEMTD/NBJsfvq4NZ0DwejY/YLWdLnJi485oeObXq08YThug8PLVX/fRU2nJxqMDnQ8q+nyUHJcaUGVzY1Jp8LjD0b3gQKUdYS/uMs7elmQFse+JidlHb3sbHDS2x/kupI0rh+Dls+CIAiCIAjDYdAoybIaONTaA8DHNXZkIBSCO88v/MzHVXe6kYBc0XTxlKSb9CzNMPHUrkbaImW5C4/bUzgnOZY3Stvo8wfHJJP5Rmk7mRb9mO9bHAs6tZLHblhEMCRPWontdDR9JhpPgNx4w6gylbVdHnLiDVj0apxeP8GQTH2kS++nM661dveQt4+ULMvUdHnIG+aXaLpJh1aloDoSDA+U1YbXNDhAru7yUJhkJNtqiN4foMoWCV473YRkORrMbiizIQHfW5UzaKCwIAiCIAjCeLtxcTo3LknnT1fNY06ykdx4A7sbP7+pUU2nm3SzLtp8Uhi+H56Vh6s/wIsHWllbkDBor+5Z+fF4/SE+qXOM+nmanF72NXVzyTjN2hwLSoU0of1ZZgKRGT1OdryBVw+34YzM6DwVvf0B2l395MQbaHf14/T6ae3poz8QwmpQ0+DwEgiGUCkVBIIh6u1eDGoldo9/RM/3aV1uHz19AXLjh7f/VKmQyLLoo8Fwu6sfty8IHMvaQngfaE2nmy8tSMXh8XGguSf6s4HAtLrLQ2tPHx5/+PHb6x3iC10QBEEQhElx/L7PFdlWnt3bzB8+rKatp4+UuKGbKlZ3esiLF1nRkZidZOT7q3Pp7Q/w9dMHd7hdnGHGpFPxfoWNs0fZcOj10nYUElw8QY11hIkhQvfjDMwp/aCyE08kMOsPhPAHQyd9bH2kDDc3khnt7gtQGSlpPbsggUBIpskZLl9ocvYRCMmsyguX09aNQXa0OnKM3IThN0PKS4iJbiofeLxGKQ3KfrZ099EXCJGfYCAvIYa2yLwoXyBEw0DWt9MdzZICBEOymC0qCIIgCMKUsCSy9WhvU/eQPw+f03jIO4VzKGGwLy+dxbfOyD6hPFWlkDirIIGtNXb6Ayc/n/4sIVnmzdJ2lmVZSIo9tS7JwtQmMqPHyU+MQQJ+/V4l5e29LM00859vHEWlkHjiyyUUfs7G99pIMJoTHxPtpLu/Ofyld3ZBAusPtPK7D6tIidNhd/sAWFuYyLtlNl480EJZRy8qhYRKIaFTK1mTH08wJNPnD5Jg1HKkzYUvEEKrVpBlMWDQKHH7AsgyqJWKaIlt7ilc1cuNN/D20Q56+wPRoHRljnVw9jNye15CDGb3sX2werWSYEhmfmoch1p72FkfLr/Isuipd3jFngtBEARBEKaE/MQY4nQq3iu3YTGoebO0nZ+vK4xWcDU4vARlhr3VSTg1Z+XH8+qhNvY3d7N8hBMrPql10O7q5/urc8Z4dcJkE8HocZJjtTx902Lu3VBBha2XgYs7gVB4P+bnBqNdHtRKiTSTLlpyu6+pm0SjhkXpJkrS42hweKnq9BAIhsixGliZbSHNpOPdMhvvltkGHe/HZ+dR3tFLaauLX148h6/9c1/0Z2fkWLlleQbffO4A8nGPMevVWA3DL/cdCBjv21BJvcNDQoyGklkmNlV18cyeJoxaVTTIzIk3YNaHg9EPKjuj9fDr5iRyqLWHjRU2UuO0LEiLo97hFZlRQRAEQRCmBIUk8ZWls3h4ax1ba+wArJuTxOrImI7oBX1x7jIulmSYUSslPql1jCgYlWWZv35ST0qsdtSlvsLUI4LRTylMMlKcEsvbRzvQKBVkWvQ0OLzRUS2fpabLQ5bFgEohYYkEhOUdvSzNMKNVKXj0+kVDPm79rafR5w8SCMoEZJlAMMRNT++jqtPN0XYXtXYP+yNlJfdcOJsNZTaOtrs42NKDDNy+KgdZlvEHZeYkG09pQ/eC1DgKEmPY2+SkPxDigrlJLEw3oZSIjnIByEswEKNRoVcrMelUPL07PEPJoFZyTmECD2yqxu7xszovnvzE8Bd5gfhCFwRBEARhirh5WQYScLjVxc5dimkAABcCSURBVCd1dvY0OqPB6MYKGzEaJVkW0XRxPOjVSkrSTXxSZ+eH5J7y47fW2Cltc/HzYczuFKYfEYwOISfegNsXpLTNxXmzE2lyenGcJBit7fJEZhCBRa8BICSHj/V5VAoJo3bwX0NOvIFKm5t6e3hP5geVnWhVCs6fk4TD4+fjWjt7GrtJiNGcMLLlVJgNap756pITbt90xxm4fUH6AyGCIZn4mPDrUUgST9+0OFqGnGjUkmjU8tfrF9Hg8LAo3YRZryYhRkP2MBspCYIgCIIgjDdJkrh5eSYA337hALsbwt11S9tcbKrq4raVWSLQGUen51h5cHPN5zaRGkqfP8gfNlWTadFzyQTPFhUmhvitG0J2pJFRfyBEfkIMcTo1Ts9nB6N9/iAt3X3RwNOsPxZcDre77fFy4w0cbXMRCIWLcPc1dZNl0aNUSNEGRTvqHSM69nDo1EriYzSkmXRkWPQYNMe64qbE6ViYbmJhuok0U/jLZEFaHJcUpzDLrMeoVbFuTtK4rEsQBEEQBGG0lmSYqbS5qevycO+7FZh0Km4Qc9HH1enZ4fLc7ac44uXxnY00Ofv46Tn5qMTFghlJ/K0O4fggb2Bu6OdlRuvtXuTjHnf8mJacEbQJz403DNoLKnNsH8NAg6JASBYb7QVBEARBEE7R0gwzMnDt47tpdHq59+I5J1SpCWMrN95AklFzSvNG+wMh/rW/hbUFCSwbYeMjYeoTv3lDiI/RYNQq6e0PkhtvwGxQ4/T4PvP+NfbwxveBjKpKqSBWq8LVHzhpme5QBh4jARmRPasDgW6SUUOMRonbFxy3zKggCIIgCMJMtTA9jn87Kxe7x885hQnMTY6d7CXNeJIkcXqOlY3lNgLB0LCynFuqu+jpC3DlcXNjhZlHBKNDkCSJHKuB6k4PybFaLHo1tV0eytt7USokMi16PqjsJBAKoVIo2FJlRylB5nEb3y0GNWqlNChLOlwD2c80k465ycZIMBoTXVtuvIFDrS7R9U0QBEEQBOEUKSSJG5fMmuxlfOGszLbw6qE2Dre6WBSZ/fp5XjvcRnKslqWZ5glYnTBZRDD6Gc6fk0Sj04skhbvj7m3y89/vlqNXK7l6URq/eKts0P3nJBkHbXxPM+lQnkJn2+NZDWpMOhW58YZIEGoblAXNjY8JB6MiMyoIgiAIgiBMA6dlWlAqJH61sZJlmWZquzy4+gN858xsTs+2DrqvLxBiV72DG5fMQqkY2fm0MD2IYPQzXHfcRnaTXk2314/HF0CrUlLV6UapkHjh5qWEZJlASCbJqBn0+F9eNIeR/upIksQ9F84h0agh0aghPkbNLPOxzmNXLkwl3awT+xsEQRAEQRCEaSFWp+L+S+fy0JZaXjvcRrY1PL3iB+sP85tL57K2MDF633qHh6AMs5OMk7hiYSKIaGYYLHo1MuALyviCAXY3OMm06AeV5X7aSMpzj3dG7rErRJfPH1wrX5QSGx0jIwiCIAiCIAjTwZr8BNbkJyDLMpIk0ecPcuuz+3loSy2r8+Kje0lruzwA0SkSwswluukOg+VTgWVpmyiRFQRBEARBEISRkCJb2XRqJd89M5vm7j5eO9wW/Xl1lweFBFkWcb4904lgdBjMhhOznDlW8cshCIIgCIIgCKNxRo6VgsQY3jnaEb2tptPNLLMejUqEKjOd+BsehoHMaJIxPPIFGNHIFkEQBEEQBEEQjpEkieVZFg63uejzBwGo6fKIKsQvCBGMDoMlkhnNjY8hxxoT/X9BEARBEARBEEZnaYYZf1BmR72TDys7aXJ6xQjDLwjRwGgYzHo1EuFsqMcX5Ehbz+c2LxIEQRAEQRAEYXgWzYpDKcGdbx6lLxACoEAEo18IIhgdBrVSwW8uK6I4JRavP8hpmWZRwy4IgiAIgiAIYyBGo2JuSiyHW1188/RMChONnJlrPfkDhWlPBKPDtLYg4f+3d+/BUVb3H8ffm4VgoyJZkiosqTboLhCEbLgkIVGROu2MSAVRQrXwYxPsIFodYgN0HEhplQIjFoiCkhTDJROlXBoqwXZoqyUmpcCkOgUpGE2jjUAuGyhZa5A9vz/45fm5Iu0Kmw0kn9dMZsh5zp7nOcOHE77PLdafb9LLi0REREREwuZ/RiVw+MRpHk6/0XrbrnR9KkZFRERERKRTjb0ljrGfu/gj3YPuNRUREREREZGIUzEqIiIiIiIiEadiVERERERERCJOxaiIiIiIiIhEnIpRERERERERiTgVoyIiIiIiIhJxKkZFREREREQk4lSMioiIiIiISMSFtRgtKSlh3Lhx3Hrrrdx3333s378/nMOLiIiIiIhIFxG2YrS8vJzFixcza9Ysfv3rX+PxeHj44Yepr68P1y5ERERERESkiwhbMfryyy8zadIkpkyZwsCBA1mwYAHx8fGUlpaGaxciIiIiIiLSRYSlGG1ra+PgwYNkZGQEtWdkZFBdXR2OXYiIiIiIiEgX0iMcg/h8Ps6ePUtcXFxQe9++famsrPyPn7XbbfTpE3NR+7Xboy76syLtlCMJF2VJwkVZknBRliQclCPpKGEpRtvZbLaQ2j4vKiqKqEu4PhsVZb/4D4v8H+VIwkVZknBRliRclCUJB+VIOkJYbtONjY3FbrfT0NAQ1N7U1HTe1VIRERERERGRsBSj0dHRJCUlnXdLbmVlJR6PJxy7EBERERERkS4kbLfper1e5s6dy7Bhw0hJSaG0tJQTJ04wderUcO1CREREREREuoiwFaN33303Pp+PNWvWcOLECVwuF2vXrsXpdIZrFyIiIiIiItJF2IwxprMPQkRERERERLqXsDwzKiIiIiIiIvJVqBgVERERERGRiOu0YnTfvn3MmjWL2267DbfbzbZt24K2NzY2Mn/+fDIzMxk+fDg5OTnU1tYG9Zk2bRputzvoa86cOUF9Dh48iNfrZeTIkaSmprJgwQJaW1s7enoSQeHIEsA777yD1+vF4/Hg8XiYOnUqzc3N1nZlqeu71Cx99NFH561J7V9FRUVWv7q6Oh599FHS0tJISUnhiSeeoLGxMVLTlAgIx7rU0NBAXl4eGRkZJCcn893vfpcdO3YE9dG61LWFI0ehrDfKUdf30ksvMXnyZFJSUkhLS2PWrFkcOXIkqI8xhoKCAjIzMxk2bBjTpk3j6NGjQX1OnjxJXl4eI0aMYMSIEeTl5XHq1KmgPuXl5dx7770MHz6cO++8M+jnn8gXdVox6vf7cblcPPXUU1x11VVB24wxPProo9TW1rJ69Wq2b9+O0+nE6/Xi9/uD+t53331UVFRYXz/96U+tbcePH8fr9ZKQkMDmzZspLCzk6NGj/PjHP47IHCUywpGlt99+m+zsbEaPHs2rr77Ktm3byM7OpmfPnoCy1F1capb69esXtB5VVFSQn5+PzWbjO9/5jrWP7OxsjDEUFxdTWlrKmTNnmDVrFoFAIOJzlo4RjnVp3rx51NTUsHr1an7zm98wceJE5s6dy759+wCtS93BpeYolPVGOeoe/vKXv/Dggw/yyiuvsH79eux2O16vl5aWFqtPYWEh69atY8GCBWzZsgWHw4HX6+X06dNWnyeffJJDhw5RWFhIUVERhw4dYu7cudb2N998kx/96EdMmTKF1157jfz8fIqLi9m0aVNE5ytXEHMZSE5ONlu3brW+f//9943L5TLvvvuu1Xb27FmTlpZmNm/ebLV9//vfN4sWLbrguK+88ooZPXq0+eyzz6y2w4cPG5fLZWpra8M8C7kcXGyWsrKyzHPPPXfBcZWl7udis/RFM2bMMF6v1/p+z549xu12m5aWFqvt1KlTxu12m7feeivMs5DLwcVmKTk52WzZsiVorLFjx5qioiJjjNal7uZichTKeqMcdU+nT582gwYNMr///e+NMcYEAgGTkZFhVq9ebfX55JNPTHJysiktLTXGGPPee+8Zl8tl9u/fb/XZt2+fcblcpqamxhhjTG5urpk9e3bQvjZs2GBuv/12EwgEOnpacgW6LJ8ZbWtrAyA6Otpqi4qKIjo6mgMHDgT13blzJ6mpqYwfP56lS5cGnb1pa2ujZ8+e2O12q639zOIXx5GuKZQsNTU1UV1dTXx8PN/73vcYM2YMDz74IFVVVUHjKEvd21dZl9p9+OGHVFVVMWXKlKBxbDYbvXr1stp69epFVFSUstRNhJqllJQUdu3ahc/nIxAIsHv3bpqbmxkzZow1jtal7iuUHIWy3ihH3VNrayuBQIDevXsD5x4zaWhoICMjw+pz1VVXMWrUKKqrqwGorq4mJiaGlJQUq8+IESOIiYmx+rS1tQXlrX2cY8eO8c9//rOjpyVXoMuyGE1MTMTpdPKLX/yClpYW2traWLt2LceOHaOhocHqd8899/Dss8+yYcMGZs+ezW9/+1t++MMfWtvT0tLw+XysXbuWtrY2Tp48yfLlywGCxpGuK5QsffjhhwAUFBQwefJkioqKGDlyJDk5ORw+fBhQliT0denzfvWrXxEbG8u3vvUtqy05OZmYmBiWLVuG3+/H7/ezdOlSzp49qyx1E6FmaeXKldhsNtLS0rj11lvJy8tj+fLlDB48GNC61N2FkqNQ1hvlqHt65plnGDx4MB6PB/j/v+u4uLigfn379rWeMW5sbMThcGCz2aztNpsNh8Nh9cnMzGT37t1UVFQQCAT44IMPWLduXdA+RD7vsixGe/bsyapVq6irqyM1NZXk5GT27t3L7bffTlTU/x9yVlaW9VD/+PHjWbFiBZWVlRw8eBCAW265hSVLlrB+/XqSk5PJyMjA6XQSFxcXNI50XaFkqf25maysLO6//36GDBlCbm4uw4YNo7S0FFCWJPR1qd1nn33Gtm3bmDRpkvXsMYDD4WDlypX86U9/IiUlhZEjR3Lq1CmSkpKUpW4i1CytWLECn89HcXExW7duJScnh3nz5lknybQudW+h5CiU9UY56n5+/vOfc+DAAQoKCoKuiANBheaX+bLtxhirfcqUKUybNo3Zs2czdOhQsrKyGD9+PMB5+xIB6NHZB3AhQ4cOpaysjH/961+cOXMGh8PBAw88wNChQ//jZ+x2O//4xz9ISkoCYMKECUyYMIHGxka+9rWvYbPZKC4uZsCAAZGainSy/5al+Ph4AAYOHBj0ucTERD7++GPre2VJvsq69Mc//pGGhgYeeOCB87a1nzlubm6mR48e9O7dm4yMDGWpG/lvWaqrq2Pjxo2UlZUxaNAgAAYNGsT+/fvZuHEjzzzzDKB1qbsLZU0KZb1RjrqPxYsXU15ezvr160lISLDa2/8v1NDQQL9+/az2pqYm62ppXFwcTU1NQcWnMQafz0ffvn2Bc8VqXl4eubm5NDY2Ehsbaz325HQ6IzJHubJc9qe8rr32WhwOB7W1tfztb38Lut3ti44cOcLZs2etf1CfFxcXx9VXX015eTm9evUKuideuocLZWnAgAF8/etf54MPPgjqX1tbS//+/c8bR1mSUNalzZs3M3r0aL75zW9ecByHw0Hv3r2pqqqiqamJcePGdeRhy2XoQln65JNPAM67MmW327/0rctal7q3UNakUNYb5ahre/rpp3nttddYv379eSfgBwwYQHx8PJWVlVbbp59+yv79+61beT0eD36/33o+FM49R+r3+60+7ex2O9dffz3R0dHs3LkTj8djFawin9dpV0ZbW1upq6sDzt0mWV9fz7vvvst1111H//792bVrF7GxsTidTv7+97+zePFi7rrrLjIzM4FzZ4137NjBHXfcQWxsLDU1NSxZsoQhQ4YEPVi9adMmPB4PMTExVFZWsmzZMp588knrgW258l1qlmw2Gzk5ORQUFOB2uxkyZAi7du3i7bffZuHChdZ+lKWu71Kz1K6+vp6KigqWLl36pfvZunUriYmJ9O3bl+rqahYvXsyMGTNITEzs8DlKZFxqlhITE7nxxhtZtGgR8+bNo0+fPuzevZu33nqL1atXW/vRutS1hWNNCmW9UY66vkWLFlFWVsYLL7xA7969rec3Y2JiuPrqq7HZbEyfPp0XX3yRxMREbrrpJtasWUNMTAz33HMPcO4Osttuu438/Hx+9rOfYYwhPz+fO++808pTc3Mzr7/+OqmpqbS1tbF161Zef/11/WoXuSCbMcZ0xo737t3L9OnTz2ufNGkSS5YsYcOGDfzyl7+kqamJ+Ph47r33XmbPnm29Ne7jjz8mLy+Po0eP0traSr9+/bjjjjt47LHH6NOnjzXe3LlzefPNN2ltbSUxMZHs7GwmTpwYsXlKx7vULLUrLCykpKSElpYWbr75ZnJzc623VoKy1B2EK0urVq1i06ZN7Nmz57y3CgI8++yzbN++nZMnT+J0Opk6dSozZsz4r8/qyJUjHFmqra1l+fLlHDhwAL/fzze+8Q28Xi+TJk2y+mhd6trCkaNQ1hvlqOtzu91f2v7YY49ZL/80xvD888/z6quvcvLkSYYPH87ChQtxuVxW/5aWFp5++mn+8Ic/ADBu3DgWLlxonbhobm7mkUce4ciRIxhjSE5OZs6cOQwfPryDZyhXqk4rRkVERERERKT7uuyfGRUREREREZGuR8WoiIiIiIiIRJyKUREREREREYk4FaMiIiIiIiIScSpGRUREREREJOJUjIqIiIiIiEjEqRgVERERERGRiFMxKiIi8hXNnz8ft9uN2+0mKSmJ9PR0pk2bRklJCWfOnAl5nL179+J2u2lubu7AoxUREbk89ejsAxAREbkSjRkzhmXLlhEIBGhububPf/4zq1atoqysjOLiYmJiYjr7EEVERC5rujIqIiJyEaKjo4mPj+f6669n8ODBeL1eNm7cyKFDhygqKgKgrKyMyZMn4/F4SE9P5/HHH+f48eMAfPTRR0yfPh2A9PR03G438+fPB8AYQ2FhIXfddRfDhg1jwoQJlJWVdc5ERUREOoiKURERkTBxuVxkZmbyu9/9DoAzZ87w+OOPs2PHDl566SV8Ph+5ubkA9OvXj4KCAgB27txJRUUFTz31FAArVqxgy5YtLFy4kJ07d/KDH/yA/Px83njjjU6Zl4iISEfQbboiIiJhdPPNN1NVVQXA/fffb7UnJCTwk5/8hLvvvptjx45xww03cN111wHgcDhwOBwA+P1+Xn75ZdatW8fIkSOtz77zzjuUlJQwduzYyE5IRESkg6gYFRERCSNjDDabDYCDBw/y/PPPc/jwYVpaWqw+9fX13HDDDV/6+ffee49PP/2UmTNnWuPAuausTqezYw9eREQkglSMioiIhFFNTQ0JCQn4/X5ycnKsFx05HA58Ph8PPfTQf3zjrjEGgDVr1tC/f/+gbT166Me2iIh0HfqpJiIiEiZHjhxhz549PPLII7z//vv4fD7mzJlDQkICgPUsabuePXsCEAgErLaBAwcSHR1NfX096enpkTt4ERGRCFMxKiIichHa2tpoaGggEAjg8/moqqrixRdfJCkpiezsbP79738THR1NSUkJDz30EDU1NaxcuTJoDKfTic1m44033mDcuHH06tWLa665huzsbJYtW4YxhlGjRuH3+/nrX/9KVFQUWVlZnTRjERGR8LKZ9vuBREREJCTz589n+/btANjtdq699lpcLhff/va3ycrKIjo6GoDy8nKee+45jh8/jtvt5oknnmDmzJls2LCB1NRUAF544QVKS0tpbGxk4sSJLFmyBGMMmzZtorS0lLq6Oq655hoGDx7MzJkzycjI6LR5i4iIhJOKUREREREREYk4/Z5RERERERERiTgVoyIiIiIiIhJxKkZFREREREQk4lSMioiIiIiISMSpGBUREREREZGIUzEqIiIiIiIiEadiVERERERERCJOxaiIiIiIiIhEnIpRERERERERibj/BXSnuvYMAoE8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import arch.data.core_cpi\n",
    "core_cpi = arch.data.core_cpi.load()\n",
    "ann_inflation = 100 * core_cpi.CPILFESL.pct_change(12).dropna()\n",
    "fig = ann_inflation.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All mean models are initialized with constant variance and normal errors.  For `ARX` models, the `lags` argument specifies the lags to include in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     AR - Constant Variance Model Results                     \n",
      "==============================================================================\n",
      "Dep. Variable:               CPILFESL   R-squared:                       0.991\n",
      "Mean Model:                        AR   Adj. R-squared:                  0.991\n",
      "Vol Model:          Constant Variance   Log-Likelihood:                11.2764\n",
      "Distribution:                  Normal   AIC:                          -12.5529\n",
      "Method:            Maximum Likelihood   BIC:                           10.3364\n",
      "                                        No. Observations:                  719\n",
      "Date:                Mon, Mar 02 2020   Df Residuals:                      714\n",
      "Time:                        11:53:29   Df Model:                            5\n",
      "                                   Mean Model                                  \n",
      "===============================================================================\n",
      "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-------------------------------------------------------------------------------\n",
      "Const            0.0402  2.030e-02      1.981  4.762e-02  [4.218e-04,8.001e-02]\n",
      "CPILFESL[1]      1.1921  3.475e-02     34.306 6.315e-258      [  1.124,  1.260]\n",
      "CPILFESL[3]     -0.1798  4.076e-02     -4.411  1.030e-05   [ -0.260,-9.989e-02]\n",
      "CPILFESL[12]    -0.0232  1.370e-02     -1.692  9.058e-02 [-5.002e-02,3.666e-03]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "sigma2         0.0567  6.449e-03      8.799  1.381e-18 [4.410e-02,6.938e-02]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: White's Heteroskedasticity Consistent Estimator\n"
     ]
    }
   ],
   "source": [
    "from arch.univariate import ARX\n",
    "\n",
    "ar = ARX(ann_inflation, lags=[1, 3, 12])\n",
    "print(ar.fit().summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volatility Processes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Volatility processes can be added a a mean model using the `volatility` property.  This example adds an ARCH(5) process to model volatility. The arguments `iter` and `disp` are used in `fit()` to suppress estimation output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           AR - ARCH Model Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               CPILFESL   R-squared:                       0.991\n",
      "Mean Model:                        AR   Adj. R-squared:                  0.991\n",
      "Vol Model:                       ARCH   Log-Likelihood:                136.522\n",
      "Distribution:                  Normal   AIC:                          -253.044\n",
      "Method:            Maximum Likelihood   BIC:                          -207.265\n",
      "                                        No. Observations:                  719\n",
      "Date:                Mon, Mar 02 2020   Df Residuals:                      709\n",
      "Time:                        11:53:29   Df Model:                           10\n",
      "                                   Mean Model                                  \n",
      "===============================================================================\n",
      "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-------------------------------------------------------------------------------\n",
      "Const            0.0285  1.883e-02      1.513      0.130 [-8.411e-03,6.541e-02]\n",
      "CPILFESL[1]      1.0860  3.534e-02     30.725 2.609e-207      [  1.017,  1.155]\n",
      "CPILFESL[3]     -0.0788  3.855e-02     -2.045  4.084e-02   [ -0.154,-3.283e-03]\n",
      "CPILFESL[12]    -0.0189  1.157e-02     -1.630      0.103 [-4.154e-02,3.822e-03]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      7.6869e-03  1.602e-03      4.799  1.591e-06 [4.548e-03,1.083e-02]\n",
      "alpha[1]       0.1345  4.003e-02      3.359  7.826e-04   [5.600e-02,  0.213]\n",
      "alpha[2]       0.2280  6.284e-02      3.628  2.860e-04     [  0.105,  0.351]\n",
      "alpha[3]       0.1838  6.801e-02      2.702  6.894e-03   [5.047e-02,  0.317]\n",
      "alpha[4]       0.2538  7.826e-02      3.242  1.185e-03     [  0.100,  0.407]\n",
      "alpha[5]       0.1954  7.092e-02      2.756  5.856e-03   [5.643e-02,  0.334]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "from arch.univariate import ARCH, GARCH\n",
    "\n",
    "ar.volatility = ARCH(p=5)\n",
    "res = ar.fit(update_freq=0, disp='off')\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the standardized residuals and the conditional volatility shows some large (in magnitude) errors, even when standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zZw4mTpyIE088sfiLSxAEQZQMUmIJgiCIrNx666147733sGjRIrz00kuQJAn77rsvzj//fNx44422YkI///nP0dPTgyeffBLRaBQHHnggZsyYgYsvvhj9/f14/vnnsXjxYhx88MH4zW9+g507dxatxE6YMAHPPvss7rrrLsyaNQv19fW48MILcdppp+Gaa67hnwsEAnjkkUdw991387Yp3/zmN/HEE0/gtNNOy/t4RxxxBGbNmoV7770XDz/8MMaPH4/LL78c48ePx+233571uwMDA1BVNa3yLwD87Gc/w5FHHgmv14sHHngAzzzzDF599VXekmjSpEm44IILbNV7b775ZtTU1GDOnDm49957ceSRR+Kxxx7DX/7yl7zPJxPXXHMNFixYgNmzZ/M+qfmM6xvf+AbWrl2Ld999F729vaivr8e0adPw29/+Fv/0T/+U8Xj53kfAqJR9xx13YNasWbjnnntw+OGH47777sObb76JpUuX8s+ddNJJuPfee/HYY4/hD3/4A/bff39cccUVaGxszHmvLrjgAsybNw9z585FOBzGhAkT8M///M+46aabeAVmgiAIorx49EIqJxAEQRAEQRAEQRBEGaGcWIIgCIIgCIIgCGLMQEosQRAEQRAEQRAEMWYgJZYgCIIgCIIgCIIYM5ASSxAEQRAEQRAEQYwZSIklCIIgCIIgCIIgxgxjssWOpmlQ1dEvquzzecpyXGLPguYRUSpoLhGlguYSUSpoLhGlgOYRAQCBgC/je2NSiVVVHaFQfNSP29xcW5bjEnsWNI+IUkFziSgVNJeIUkFziSgFNI8IAJg4sSHjexROTBAEQRAEQRAEQYwZSIklCIIgCIIgCIIgxgykxBIEQRAEQRAEQRBjBlJiCYIgCM5gXMLWvli5h0EQBEEQBJERUmIJgiAIzuylu/Cfr6wr9zAIgiAIgiAyUjFK7KOPPoqpU6fi97//fbmHQhAEsdcSk1TEJLXcwyAIgiAIgshIRSixq1atwrx58zB16tRyD4UgCGKvRtV0KJpW7mEQBEEQBEFkpOxKbCQSwS233IK7774bTU1N5R4OQRDEXo2hxFKDeYIgCIIgKpeyK7G//e1vcc455+CUU04p91AIgiD2elRNh6KSEksQBEEQROXiL+fB582bh507d+JPf/pTQd/z+Txobq4doVFlO663LMcl9ixoHhGlYiTmktfvhQ6gsbEGXq+npL9NVC60LhGlguYSUQpoHhG5KJsS29raivvvvx9z5sxBMBgs6LuqqiMUio/QyDLT3FxbluOOJrquQwfg9ZDwOlLsDfOIGB1GYi4lUwoAoG8ghqC/7ME6xChB6xJRKmguEaWA5hEBABMnNmR8r2wSyqpVqzA4OIjzzz8f06ZNw7Rp07B06VI899xzmDZtGiRJKtfQ9mqWtA1ixkOLETUFWYIg9i5YPizlxRIEQRAEUamUzRN79tln45hjjrG99pvf/AZTpkzBDTfcgEAgUKaR7d3sDicRk1QMJRXUV5U12pwgiDKgciVWA+Ar72AIgiAIgiBcKJuW0tjYiMbGRttrtbW1aGpqwhFHHFGmURHM+6Lp5IUhiL0RlTyxBEEQBEFUOJTwRNigUEKiGLb3xzF3RUe5h0GUANU0YKm0BhAEQRAEUaFUVLzoM888U+4h7PWo5IkliuDdjT2Y9cVOfP/rk6go2BiHPLEEQRAEQVQ65IklbPBwYq3MAyHGFIpqTBhJoYkz1jFvJfWKJQiCIAiiYiEllrDBvDAUSkgUAjN+SCopsWMdFk5MnliCIAiCICoVUmIJG1yJpXBiogDYvCFP7NiHDFkEQRAEQVQ6pMQSNhQSYIkisDyxNG/GOvYWOwRBEARBEJUHKbGEDSrsRBQDy58kT+zYhwo7EQRBEARR6ZASS9jg7TVIiSUKgHntKCd27MNzYsmrThAEQRBEhUJKLGGDCa4UTkwUAhV22nOgXtEEQRAEQVQ6pMQSNpgXhtLhiEIgJXbPgQo7EQRBEARR6ZASS9jgXhgKJyYKgKoT7zlQTixBEARBEJUOKbGEDSa4aiTAEgXA5k1KoXkz1qHqxARBEARBVDqkxBI2KJSQKAaWSy1TOPGYhxd2ojWAIAiCIIgKhZRYwga12CGKgaoT7zmQIYsgCIIgiEqHlFjCBvO+UHcNohAUyondY6DqxARBEARBVDqkxBI2yAtDFANVJ95z4DmxZbZktYcSuPq5lRhKymUdB0EQBEEQlQcpsYQNCicmioFXJyYX/pinUgo7beiOYm1nBDsHE2UdB0EQBEEQlQcpsYQNJrhSKCFRCBROvGeg6TrYk1/uNSClqACApDy6c+rq51bhxVW7R/WYBEEQBEEUBimxhA2VWuwQRcBCT1MUTjymEdMIyq3EMuU1aSqzo8WG7gi298dH9ZgEQRAEQRQGKbGEDQonrjxeXdOJULyy8wKZB18mT+yYRlRiy50XnzLnUmoU55SialA0veyh1ARBEARBZIeUWMKGVZm0zAMhAAB90RT+8P4WfLilt9xDyQoVdtozUCrIE8uU19EMJ06ax5Qpt5sgCIIgKhpSYgkbCnliK4pkGbxRxUBK7J6BLZy4zIocz4kdxXDipGwcq9wKPEEQBEEQ2SEllrBBLXYqC+YRKrdCkQs1z8JOqzvCWN8VGY0hEUWgCsYrpcyGrHIYcBKm17fSnzeCIAiC2NshJZawwYRY8sRWBsyzWekeTiXPFjv3LdiGBz5pHY0hEUVQWZ7YcoQTM09sZT9vBEEQBLG3Q0osYYMJrpXsie2LSXtNuJ9sKq9yhZ8vmze5PLGRlIKeSGo0hkQUgb06cXkVOeaJHc1wYu6JrfDnjSAIgiD2dkiJ3cPQdR0buosP12SeWLVCPbGSouG7s77E2y3d5R7KqMA8sErFe2LN0M8c44ymVPRGJegVOr/2dsTnvtyGrJRcjnBi0xNL4cQEQRAEUdGQEruHsXxXGD9+diV2FNnn0OoTW8pRlY6ErCImqeiPS+UeyqggK/mF6ZYb5rmScyixMUlBUtEQTY1u78+xzNvru/Hy6t38745wIqfHu1jE21dubyTzwI5qODH3xFboAkgQBEFkRdP1su9fRHbikoqNw3C4McqmxD722GO49NJLcdxxx+Hkk0/GjTfeiM2bN5drOHsMkZQCABgy/18ovMVOhXrKZHXvKrzCPLG5lMNyouk62H6RTblKKRovVNUTHTshxV1DSfxjQ0/Zjv/mui68uqYLgOGR/+FTyzFnefuIHEutxBY7ZahOTC12Kovlu0K4fu6qio9IIYjRZF3nEM7622IM7iVG/XyZvWQXrnx2RbmHQWThtbWduPr5VcM2yJdNiV26dCkuv/xyzJ07F0899RR8Ph+uuuoqhEKhcg1pj8Dq81rcxLA8sZUpxDGPZKk8Je2hBB77bEfFhreOBaVdVHyyeYxjkmVY6YuOnU33jXVd+O3bG8sWXhuTVD4PIikFCVnDqo7wiByrEpXY0Qwntgo7Vdbzpmo6/vPVdVjRvnfuj+u7IljZMcSNtOVAUTW0DRQX4UQQI0HbQAJDSQW7w8lyD6WiaA8lsCuUKPcwiCxEkgpkVR/2/l42JXbmzJm49NJLccQRR2Dq1Kn405/+hIGBAaxYQdaT4cCE0GKVnkpvscOVuhLJtR9v7ccTX+xEOFE+4SgbTCms5OrEosCfzaomhhCPJU9sQtago3wVu0Ulll3D9V3RETG82KsTl3fOlaM6caUWdoqmFCxqHcDqjqFyD6Us5Fv9fCT5YHMfLntqOYaSctnGQBAiKcG4SVhIqoakrFWsc4KwZNpcdVRyUTE5sbFYDJqmobGxsdxDGdNwJbTIh1cZ5vdHGrnEnlimdFWqkmiFE1fm/QDsBpNsC1JU2Gh7x5Anls2RkVRsnlvejq29Mdf3YpLKhfeo6c0OJWTsHiq99V2poMJOLLR3dKsTV2aLnXKnFei6jpXt4bIJhVyJHUWvvJPBhAxF0/coheG+BdvwipBvX240XceDn7SifYx50ZbtDKFzBNbjXLDnIUI1JmykFMPwPJpRPERhSHl2tMiFvxSDKQV33303jjrqKHz961/P+Vmfz4Pm5tpRGJXzuN6yHLcQgtUBAEBVTbCosTLlNRD0V+S5VsUMK7gv4CvJ+Dx+HwCguq5q1M63kHnkCxqPqKdMcz4f1JilkKqannGcngFLMBmStYo9Hye6z7D11TdUo8F8vkqJqun4y8etuO70r+KEwyemvR+XVNQGjfmu91vXsG1IwvQSr0m1Q5aH3OMrzTNWLDLPz8fojcNnrAfaaB4zD8LmPu/xj9w9ybYuLdnej+tfWI2XbzgF07/SNCLHz4Y/YKyD1bXF7WulIGCuxcHq8o2h1CxqHcCQ1ISr/7m051OsrNQ9lMTTX7bj4H0bcMyU8SUd00hy+yOf47xjDsD/nD9tVI/rDRjrleKtXPlgOBQ7jzSPBwAQqK1Cc12w1MMiSoDHlKuGu6ZXhBJ7zz33YPny5Xj++efhM4WIbKiqjlBoZHNTYpICr8eDmoA1nubm2hE/7nCJRA1rYHgoWdRYmVctnpAr8lwHzDGVanxDMUNo7x+Mo3EYcQmSoiHoz+8HCplH4YhxP2PJyrwfADBghgb7vB4kZTXjOLv6DU+j1wO098cq9nycRMyiGf2Dcag1pVdiWa7wQCT9mVU1HQlZhc8LhEJxdA1Y3tovW/tx3rEHlPQ6hgRvQiJV3jmXkAzvQiypjNo4Qub6mVK0ipqffeZ9H4pJIzaubOvSpnYjB7u9N4KD6kv/DOQiyp7BUBwTq3LLCCNBxNwregfLN4ZSE5cUxEdgbylWVuo1PbCRaKqinr9s6LqOcEJGdyg+6mMOm3tvz2BizFyvQih2HsXMkP/uvih8cnWph0WUACZX9Q3GMS6QXXaeOLEh43tlDyf+wx/+gLfeegtPPfUUJk+eXO7hcH7z5gbc+8GWcg+jYJhnvpjQR10oS55v/p+m63ji87ZRq47Hw4lLFF7Lw4mHEdKwtG0QM/62GKF46XOl2PmOdDjx8l0h/PvTy4sKv2Fzpi7oyxqWzZS1rzTXoDc2dsKJUyMcTszyMONSekgYe43dfxaS3VwTwOaeaMnHUomFnUa3OnFl9mVm16Jc4cS9pgJXrvA8Ft5dzpxYtueM5nwcaSRVq6hUFbZ/lDuVoRBSigZNB4aSox9mboUTl/7Ymq7jgYWt2DU4tkK7Aeu6sPQQovKQS5QiU1Yl9q677sL8+fPx1FNP4dBDDy3nUNLojqTQHxt7BRxYOHAxm4D4lXwF2F2DCTy2uA2fbR8o+HjFIGul7ePIcjiHkxO7tS+GlKJhMFH6+SKpoyNUr++KYHNvDKEizoHNtZqAL6/CTlPG1aJ3DBV2Yuc0UoIVy/10U2KZ4m8VdjL+nrZ//YhUX6wUJVbXda4sjKbiZOXEVpYQXY5KzSIsh71cCpxaATmxbO8ZzUJjI43R9qxyzmc06g+Umpi5bpcjV5rJB5ERUKD7ohKeWdaOj7f2lfy3RxqrKCApsZUKM0iO2cJOv/vd7/DKK6/gvvvuQ2NjI3p7e9Hb24tYzL24yWiTUrSK7ZWaDV6duAglTxRg822xk+QegtG5VpLCzq/46st/+XgbeiIp8/eGr8QyxW8khAFurRrhTZ1txMUs+sxDURv0QdUz3xumgB06oRZ9UWnMFF1IqZkNJ0lZxS9fXTes1htMeY27XHt2XzTzusZMQ8CR+zWgayiFVIk3abbmVfm9UMso3Cqa1Xt4NJWGZIUK0aWIGBkOTIlNlUmBs6oTl3FOck/s2Fi3cqHpOmRVH/G9pRCsqJexc43Z+l0OT2xqBD2xrIhg1MW4OlIY6/7w52OKe2LHzjza2yjVnlY2Jfa5555DLBbDT37yE5x++un8v1mzZpVrSDZSilZWIa5YFO65K3whEAW3fBeS5Ch7LqwWO8Udb0N3BM8t78Adb28EYAlFpVBiR+IaMKV9pIVXrsRmOc7razuxdnd6iw923rVm/ngmZT4mqajye3Ho+DroQFn6uF05ZyVeWdNZ0HeY4O52Wm0DCXzaOoDVLtclXxJZPbHWa7KqIZJSUBvwYcq4GugAdpY41IsZsqr83pK1sSoGJoT4vZ7yVCeuoBBLwLWKRa8AACAASURBVHouy6XEsciJcilwCk+rKKcn1lRi9xDvDvd6VpCckxrhqJeRgEXLZFIk13UO4e+Ld4zIsUcynJhFTsVG0cN82exleH55x7B/h62TFE5cuViy9/Ce9bIpsZs2bXL97+c///mojuPnL63F62vThdqUopVViCsWFk5cjEJVTChhcpT7KrKQrmI3OVaoq9NsDl4Ka9BgPD9P7I7+ON7b2FPQb4+WJzZubsTZBLS73tuCq59flfY6s5rXBI1rm8nDGk0pqAv6MGWcUYluON7LYtnUHcH6rkhB38mWpxUyC0jk41XWdB3Pr+hAnyOUOpk1J9YSICRFQzSloL7Kh4OaawAAbf2ljVyxK7HlWwCZstRUE4Cs6qMm1CZHoMXOYFzCj55ZMSyDQ8pU5MulxPaZOezlUuAqIye2vCHdALBm9xB+Om91SZT51ChHUeWDNEwjdTng4cRJxdX4/+HmPjzx+c4RaU81kuHELHIqOkpKrKbraBtMlKS9kuWJJSW2UhnznthKYdmuEDZ0pxdISSlq0b1SVU0vWz893id2mEpsvl9nXpLRsubKJQgnBoB+sxBVKaxB+Xpib3ppDf77rY286mo+jFZObC5PbLaNjBd2yuGJjaZU1Ff5cdA4UwEbGF1PrKJqUHUUXICLKRBuik04kb8Su6U3hvsXbMMvX2uxKQPcE5slnBgwrmtUMq7h5H2Ma7ijv7SGALsSWz5Bkl2fpmqjgP5oKQ7MoFBKwX5bXxybeqJY3REu+jf4OjXC12Ht7iHc8MJq23EUTUe/qcSWr7BT6SNSNnVHsao9/3vCPbFlVGLX7h7Csl3homoXOBnpgnXFUIljygUzPuoAT/cQGcmepSMZTsz2nugo9aCN5xENli9svdyT8tf3NEoRBQns5UqsrGpQzBYWIpquQyrS+p+QVfzLw5/jk239pRpmQVg5scWEE1uTKd9zH21PrDSMcGnAGqfsaLQ8HMt2vp5Ydok2defvCZRLFHKRCysn1v0cWE6cG2yu1ObwxMYkBfVVftQEfNivoQo7BuLoHEqO2txhua2FFuDKVtgplMjtwWYwT9yG7iieXdbOX49nCycWBAhJ1U1PrB+N1QE0VfuxY0Q9seUTJFOCJxYYfkGhBz9pxcdbchcoYXuBjtKFNIZNb33PMIqZWYWdSjOmXYMJPL8iPWxvSdsgVrSHeTViwPAk8/zkciuxJTTmPbp4B+7/eFvBYyhnOHEpC3yxa1nKEG1d1/H44jZs641iyY5BzFtZWGjoWAwnFtftoVT63sKMoCPhFWT3cCS8pdwTK42OJ5YdrxSKZz6e2PVdkT0mNWAs4pTBi2WvVmLZBHc+NMOpRhpJKoiklLxDx2S1tNUBlWEpsUXkxHIv1WiFExdfuMr4nn2cKVYhbRgPklXYKfs1OGxCHQCgpTP//ElplHLBmLKUyqAssEJYdcH0/oiKQ4nNdB2iKZV/f8q4GixqHcAFjy/FK6t3D2/wecLucaFejGSW9SBcQDgxC59uqvZjd9jqx2pVJ1bSIjhissMTa4YTA8DkfWqws8Qh2Sz6JOjzllWQ5EpsiTyxr6/twsI8DItJhweyFITNUL+eSPFtpUrdYue9TT24f8G2NOGXhQ2L3hfRgOW8D9GUgu889gWW7wqVZFyZGImc2KSsFqQUs2iYcnpiSxlWzudUCZ/zuKzi75+34e11XXh9XReeWrqroO+PzerE1jPkFtY7kkWG2PUaSqbvHcPFCiceHUUvyj2xwzseK1gGZFZiB+MSrnpuJd5a3z2sY40UfdHUHq9gkye2BLBFxfnQZBNac8FyNt28Kk6iKQXffuRznPp/izDri50FH8sNtvgXFU4sLIL5fj9RhCd2IC7xB3RHfxyn/3VR3nkQ8jA3OVH5VTR92J5YRdO5kJpLGBhfHwQArC+gCNBwC1nlS1zObgVlXqRm0zMmwqsTB5gn1n3uM08sABy8Ty0PgeocGpl2OyvaQ9ghKHlSkUpstjwtt3Di9lAClz+9nOe+PvF5G65/YTV2DiawX0MVxtUFbaHD7BlS9XSPu1hUQ2ae2KBxDSc315Q8nJidY3UZPbGvrunEvFWGYYN7YocpACqanpdQkHQYDUoBmyP5eGJlVcMfP9iS1oKKe+BKNCYm5DmfBWasEpVbcSzOa9gVSaEnKmFz78h2FbA8saWbk5KqFxQ2zsZQrgrNgFDgqwSKNA/dLaFhgKX7RJMK4pJacFj+cCOtyoGY8uFWoXgk8zNTQnpVqcOVozyceHQ8sWyvG66RSHw2Ehl+a+dgomy9fXOh6zp+PGclZpZIJ6hUrM4gY7SwUyWQyBBCyUNaHJatpTsGsD2H0MgWbbf8Nid9UYlbubb0puflFsNwWuyIG0e++cC8EEoBE/Ha51fhSdNCuzOUQErRbJ6pbBRT+IGFjQN25bwvmuK/V+wGEBaEwFzCAHu/ME/s6OTCxV2soG+v78aPnlkBwPLGuCqx5jU9oKkaADLeS0MBMxTdg83iTgDg9XiGO3xX7n5vi20jSAlW60IEt2zeAWbAEOfPivYwtvTGsKXPEOzXd0Wwsj2Mle1hHLRPDeqCPluYsCjcxB2hW6KAJKkaoikVDaZ3ckJdEIPx4r17bljhxL6yVS19bW0X3moxLORN1aUJJ1Y0Pa81OSEbFbTZd0oBE5S6I+5K7OOft+EfG4yCbzsG4nh5dSeWtA3aPpMqofICWPvUgCM/nD3nbp7YuqAvTcBknqeRrmDKCzuVcB0U94X8Ps9yYveQcOIRKOwkhrfGJKVgL8tYbrEDuOemjmTPUvH6ljovNjbKhZ2Y0jxcI5F4TTJdcxYpWYmFn8IJBb1RqeSdByqNUsm2e7USy4Qa50TOVFzgv19bh5lftGX9TSVHGIPb8YHShSjlCie+ff4GfLZ9gP8tqxpufnktWroiNsU13z0kmWHTUTUdv317Iza65H92RVIYMMPWmCEh342UhxMXsPH+xyvr8JcF28xxWt/rGkoNu0KjmF+ZSyBiCsLm7kjeXh4eTjxqfWKtcW3tjWFTTxS6rnMvkpu+ye794Wa4dGsGQ0/MLEoEAN88dBzOPWpf+L0eWzhWKUnKqk24FgW/UJ4WWJYfD2QIJzbvvyjYdphRBSxXmnm7uiIpHMyUWOGcxTCzmCOCI55W2ElBnemJrQn6kJQLE8Rzwc4xWEZPrKjIN9eY4cQl8MTmCudTNeNes5D3fM9/RXsIX+wYyPg+98RmUGJfWrUbH5r5urxnsORuWC2Vd5g9s4MOJZY95+L87BxKwuf14IDG6jTlKTJKIYcjkRMrqVpGQ42m62mFuNg1K284cWlC8MTfkkuoMIrVcmOSWvB8HYuFnfL3xI5MODHbkkvtVRQ9saNRqNTyxA5vLRGVokwRkSzyrxILP7WHjbGx1I49Fe5AonDi4uE5sYpTYDBedwqt4YSc0wLKNoR8FixR0S2VlU7NouRpuo73N/XiyzYrf6kvJuHzHYNY3RG2t9jJ2xPrHv4zGJfw7oYeLG2z50pJimZrsM6uQb6bXTHhxDsHE7wnqTjO7kiKL3jZHqSnlu7Cwq3uRWHESre5zkEsKtWWb860ENpeiibgbui6LlQntnv+2LGZAO5mNWPnVV/lx6SmalcldsdAHDFJxUQzpPqAxmr8/rwjcUBj1YhZeiVVt4UTiYJfvhWKnVVanbh5YjtMTzRTXkVDx0HjalEX9NsayCdk938DdmUiaobnsZxYpmwlJBXzVu4uyXXkSmwZc2JFwaMUnlhdN4r05aoKzo7RYBpa8vVEP/75Tjz6WWbjJhMuw0klbZ3XdB2hhMzHlshgWJVK6IEDrPscSliCkqxqXKkV59K6zgiOmFiHmoAvbfzcEzvCxV9GIidWzhJO/MWOQVw7d7UtXJ99NpXHXr2kbRA/fnZFyWsZWB754T+bbM/Lx4Abc8nXd4P9ViQlI5ZSIKmFdWrI1s6sUolLKl+Ts+fEjkA4saphn1pjjXx3Q48tfWa4MKVS1UfHcMM9scM8ljjWTNd8VyiZ9f1y0m6ObU9XYtlaIZMntnjYBHZuSm4V8nRdRySp5FzweUJ5HjmxTFhrrPbnvUi8vHp31rYAvMWOruP2+Rvwfx+38veYIMTyHwHLihiXVHthp3yrE2co7MQWJGcIHztnJiCy9/O1LBdT2CkmqdZxRU9sxAonzvYgPfTpdtzy+nrX90QFJdfcEI+dr9FCvC7DCftavH0gLc+OkVI0Pm9Ey6To1WchhW5jYN/1ez04ZHwtWl0q5j6/vANBnwffOXo/2+v1Vf6MXpzt/fGCe7qKyKpmu842T2yGvNgd/XH8+9PLXXNds3lisyqxgsJshROLnlgxnNipxFp/s+gF5s1mPY9buobw54+2Yn7L8ItUsGiMqkD5PLHiOTeaodPDsZirDoNZJtgx2DHzPf9YSsmqZLPiX0B6le+hhAJNT69Q7Vw3S+mBA6xzE+dmbyQFdsbsmVRUDS1dEUyf1IjqgDdNwBwaZU9sKfP+JCVzFAN7doeE55RXJ85jDC2dEWzojpbcO1bK3Oh8vZ4xScF5jy7Bgq25C6M5PbH5/H4xY6okYpKK8bVB+Lwe23xhjKQSKykaxtcZhuHZS3fh/gX5V9vOhWjIGo2QYu6JHeZ1socTuz8n3BNbxqiKTLCx9ceksrXqHGl0XS/ZnraXK7Hu4UFuSmzK3PByWVZlh3K2pG0QMx5a7LoIsEVtXG3A9rCtag9ntEQ+smgH3mzpynh8JoQqqo6tfTFsFnJteb6ulC40J2TVdsz8W+ywjcp+XeIOzwKDlWt3Vo/Ld9OSC7TU6rqOuKTw+yEepzeaKuhBGnDJPxSFwFwhv+LcyVtpF5TGYq36uq7jltdbMHeFexXgTGHtYi4oCzN0E57YNfX7DCW2bSBhu86hhIy31nfj3Gn7YVxt0Pbduip/xg3ywU9a8ccPtuRziq4422eJAnCmNjtrOoewuTfG81HE++TuiTXDiYXjdIQsJVZSNMQkFQc0VsED4NDxtair8tsUNfG7znDimGRVdGZzzemJZVVv15m51qqmYyhZWPEqBnuuylXYSdft94zlYA9HeVHyVGLZ+3XcE5vf+UdTSta8nnBS4Yqxs7gT61ftVF6dRlD23LHQ9mhKwZxl7UULOWwtEZ+D7iErl509k1v6YkgpGqZPakSV35u2V0aTpWnD8eqaTnQNZa6L4GyNVgqkLJ0B2FywRWIUUJ2YeaZLXWyHVyfO8bv5FDITuzBki/IJJ4z9szOPuhWyoMQyQ3YhQupIKbExScFP5qwsWe0RkbisoDboQ2OVP4Mn1r32St6/L6kZja6SquGAxmr+dynrS4h70WhUKC6VJ9ZW2MnlGdB1nUfmVWIF4HbzOUsp2ohc955IqqQe+2JQHLrVcNi7ldgMhZ14dWJhYWcPWL6KCnt42kMJRFJKWgENwBJc9qkNckv+pp4ornthNR78ZHva53Vd5yGFmWCCF1O4RYWVbSYxmxKr8LGIimH+LXbcN52Y8Lsi7G+u7EuF5XoVWoI/pWhQdcEDLCjbQ0mloFyrL3bYi608/nkb/vzRVv53zsJOmg6vucfIeYaD2ZSoIgU4VoUzUxEgsciQrTqr4P3I1guXjcvn9eCQ8XVQNB3tQrj0xu4IUoqGc4/aN+279UFfRgF4IC4Py1IqqxoSsoYlOwZxweNLbF4RZy4gg3lWoy5CqNNwomg632TY52KSwhWDwbjMhY8fnTAZz/34eOzfWI26oA9xSeUKSELWEPR5zH87lNiUwhU59rsNDk8s6+m5rtPwWr+1vhsXPrG0KKNHufvEJhUN4mFZdeJ8ijJlwlJis18PNtdY9ee8PbGSmnUjDidk3l7LWdyJzUPLE2s3gjJEw6qi6Vi4tR//t7AVOwaKK/7Bzk0UjsWxsfm/psMwjEyf1Ihqf3o4MfM8xYYhbCVkFX94f0vWSAJe2MllTmu6jplftLkaGbMhprWkjynd42lVJ859rs51oVTkW+Drsc924PoXVuf4Les8su0tTDZxO5cPNvXaK8Cb16s/JvG1JN+9Dhi5FjsdoSRauiLY0FV6JTZmto5rqPaPSHXiBz5pxc0vr3V9L6VoOHifGjz6/ek4ar/6khYdi6Ys49uoemKH+cykbEps+m+FEwp/PisxJ7ZD6NQxEiHFD3zSil+/4R5Z6OTZZe34//+xqeRjsEcYkhJbNGL+kWjRdvPEsoc4tyfW7u1km4ObxYcJKuNrA/xhY8rjOxvSN3QW9plNieWeWM3IPXVTYjN5YhVBgM1XX8qUE8uEmvQcP7sxgFu8he/HJRXvbujh10zTdewOJ9EdSQnhxPkLmOJxxHYwohciW5lvVq30c4cS+8IKeyP3XGNSVJ0rHrKm4RevrMUba7uwcGsfLn96uauSmct7OxiXcm4w7NwzhbaJ80HcQGRhvrAzc8+JNV7zez346nij6nCrINgwA86EumDad7OFE4eTctG5UYZ3wXjuNvdG0TmUQqfg6QlnsGwzoT7mIoQ6xyJ6O9l1Y15Ydgz2exPqAjhsoqHI1AV90GFtsAlZ5SFhaYWdZJXnPLH5wQo7sb68LES1I5zEQFzCrsEEoqnsilUmnH1iRzucyXn+zIPdFy1+M2fPfC5FmCknDdXWM5oP0ZSS8Vrruo6hpILDzXvvLO7EFK+0nFinJ1YQTmVV4wWVilXu2TURjTnME9tYbT2TazuHMLE+iP0aqlDlEk4cSdqNPsWQT//mbDmxu8NJPPpZGxY6wl1bOoeyzl9J1TLOcTdPrFWduBBPbGk9KfmGE3cOJXNWN02JUT5Z5jrb493O5a73NuP55dY+yBRWcX0tpHCUW/eBlKLhySU7hyXsutV8KBVxWUVd0I/Gaj8iqfQ5PFwltj8mcc+hiK4bLQKDfi+On9yM8XXBknruoikV+zVUGf8e4Zx34xjWnjuc+h/sejdU+V3v907zWnpQ3krjmWgPJTGp0bjufbHStx/si0noHErmtbevag9j2c7S9wAX19XUMPP792ollgkAOuxKTMolz5MrX7lyYh3KWbY8GqY8NNcEBIXNeM/NcxtzeDHd4DmxpidWLLjBc2IFAYnnxApVTgsp6pIpJ9aZ42UdzxiP4vBYy6qGu97bjD9/uBXvbujGb9/eiH/7+xJs7YvhP19dhwufWIofzF4m9LYrTIl15uc01fhtApz4UK1oD+H+Bdu40sC+s3j7gM3bOq42iNqAD785+zB+DtlQNEuJlVQdy3eFsXp3GB9t6cOW3hjufGdT2uItqTr8pvvWTRi45fX1+N8ceTDsGoczhJjGhBxpe59MNneN1zxwn/88nNjrwUH71AAwrIkLt/bjkUXbuXDqDCUGmBLrvkGGE4W1whERIyLYHBSF5EzhxGJhGzFvA0if4+z3fB5r42T5sJOaqjGYkPlxmmut1kQsDJg9CwlZ5dcm7XlJqdwTy4pIMeWV/b9fsNau3R3hCk4x3gzREwsgb2NWqWDn74Exn6oDPkysD2J3llDTXLDnhq2JmWCKATMS5LPGSIoGSdUzKjZx0zi4X0MVAj4PIg4hcyDNE+uunNo3fY0/M8WGw7F9SnwmeiIp+L0eHNhUzX+/PybhwKZqeDweVLuEE7PzGU6LnXz6N2fLiU267GubeqL4yXOrsLIjc/2IbD24ky5KLDPWsTE8sLAVs5e493KMCQJ5Kcm3T2zKTGPIto+nXNZ692MyBdDl2suqzVvkZmgtJpxYHPeK9hAeXrQDK7PUAskFbyE3Ap63mKSiNuhDQ1UOT6yi4bPWgYK9mkZ/8HSjpKLp0GGt1dn20mKISgpXYt3CpEtNpk4ChcLmW1ON39VwwHJOJ+9TU3GeWPY8fe3AJgBAf6y4tKBsRJIKErKWZjB2IyGrI5I3LOpb5IkdBpmqA7N2DsV4Yi3lzL5BugkbCVlF0OdBfZVV2Ek8pjOvzfJiZh6DWIxHUjWbMOTWw5Z7YoUNL+Dz5G0Jc54nI5rJE5uyGwMsJVbH5p4o1ndHbArGtc+vwuLtgzh8Yh1iQm4IO97qjjBmPLQ4owAUF8JCVU3n59hUHbB5PsX7+srqTjy/ogM/emYFD7Oeum89hpIKlu2yrFKRlIJ/OXIiLvnaJPi9HpsgoOl62oaiaDpXPGTFaO8wlFQQ8BmP4Rc7BrF2t72HrKxqXOlxEzR6oyn0ZSjYxLCUWPeNiN2TgM9jz4l1zOXaoM9VILGUWC/qgj54YFhVP9jciznLO9Afk+H3engup0i9GVrrnG+KpiOSUooOK+MKuLBYMwW1ocqfMZyYe2IlFd+fvQyzzX7GgFu1cuN6jq8L8uebKbFH79+AUELhVZD3qbEUeKYkidEKzBPrZvRhIbVsbGw+1AbsnljAyItlglSh1+79Tb3oiUrwecANJ6PdK5Y9r8dPbuIhuJOaqvPuIy2SUjSs2T1kuw6ZWi6wzwOWcSCf68d+T1I0V8s2uxdN1QHX6r7ME8tqLsQF77zb2ACz1VKe4XDRlOLa2ofdV9FYGorLaKoJ2KIjErKGanOeuYUTM09sPgJRJtiawp4nN7LlxLoZZ5knMJMQqJiRGpl+0y2c2FpTjOMsah1I6+fLKFVopBN2rrkUQ7Ffa67PANmfc8sT61CiVCNVR1Ri3eSjQsKJ3YzULCrB2Qc1mlJw4RNLsXxXbk/RSOUoA8a8qw36UO+odQDYC9h0hpP4j1fX8R7Y+cLz1x2RWux3g6b80FBCJTalGJF8+3NP7Mh7LMVjDCdXlT0jzTVWhKPIrsEEvB7gkPG1FVeduHPIWKunT2oEMDLhxOw5chYZdCMhayV/ZhZu7cOKduuZpRY7w0Cc4OJm42YNjOXp3XAqZ1ZlTA0PfrIdm7qtnIy4pKIm4EN1wPB8KqpmO+ba3fbKrDFHUSQ3RCWWhRMz4co1J1YIqRbDifMVgDMVduI5sY5FJMaUVs0edi2rGiRVQySpYCipoCbgxX0XHY2kouGEg5rxvX+aBACCEmv87pbeGCIZBDXnuYrn2FjttynLKZf73xOVuAfo9EPGoTbgw4ebrVY7kZTC8xMDPrsSO39dN85/fInDk6dxTyzL1Q0nFZuV07lRS4pmKb4uD3tcUnPm+rH3M4XQsnswrjZor07MCpmwgjdBnyn82ecGr07s88Dj8aCuyqi+y8IsW/tj2Kc2AI9L0Yn6Kj90pCsXTDguWok154cOy+vK/r9fQ5WttYhIyBSkBxMydgwk0NJpGRWcbafCwu+x+9wfk1Dl9+KgfWoQTsi8cM8+NYIntsrpidXQWO2H3+uxF9mSVUiqjvGml5Ydj80hK5w4xcfRFUlx41chodhxScXt8zfgzXVd8Hk98JuCUa7rH4rL2NqXXo26WNjzevXJB+GZK44DYLRj6izCE/vBpl5c+/wqWyhyNqGFCT9Wn9jcmysLs9Phvi6ze9ZY7Ue135t2fFGJTMoqF9idz4Nzf2LHzRUO98iiHfjZS+n5dGJOLNsfBuMymqr9hhIreISZsYSFE4vKOs+JdTFEZWLx9gFbHiW7bvl4Yt2UN/bsic8Ofy1DGKS4lrrdZ/dwYnM9NF8bSikZlfdSFalxwn4vl3LMPufcT+yfse6Xohmhqb97dxM6wvbQVXYs57kw4bPfpsS6GBnMz+0aTLh69JKymqaci/eEX2/HdzuHktgdTuLLPMIdRzKcOCYpqAv6UBPwpilfsqrzVBzmASw4d9uc+/0Owyu7VkHuifWVrKcr25tY0ajhRFoAwMp2I+Is6zFL5Ill81KMcBTZFUpg/4YqM9y49EaNle1h2zNRCMwIcUBjNar83mGl0WSCPUedQ0k88+UuPi/dSCql9cTquo673tuCJz63IlhyRZXkYu9WYjP0amQPkA4rRzUqpW9qbrCNjlnW2WIcTsp4+std+GBzr+2YtUEfF0qTil2Jbemye+Xy8QaLfWJl1SiSwiahlePo0mJHqE4c9HkLaLGTbjk1juGe2xVznIMYTiwpRq7XUNJQDr92YBPmXnk87r/oaN5WhHm2mFDDNmkWYpdeYMrudbaU2EDGyr+issfOoy7ow+mHjMPHW/uhmht+StEEJdZr23i3D8QRTak266mRE2s8ckyZH0rKGErK3EvpXLwNT6zf/Hf6PYlJah6tQ6ycWLcNjm1Y42oDjj6xlgEGsJQm5zMghhMDhqcxJqn8Xq/vitiUOBF23k4LMvPKDNcTC1hCFps7hhLrvikz5ZYVVxAtoc45zube+LqgJVzKKqr9XjTXBKDD6FHs9QCNNX7+PXY/2ZqSlA1jVq3plU7/feaJzRxOXOX3YkJdEKG4zDepQpRYNgc03SjQxT2xOX7jyaU7XZWkYmHnXxu0rtcBTdXoiaQKngvRlAId9jYp2TyX7B6ywk4doSTe3dCT9RhiQSM3wYtFPzTVGJ5Yp8FJjAiIS6rNqCgiKRqYCUhSNX7cXJ7YgbiM9nAi7bkXw3MtI5dkeGKFFlDG3DTWrGq/Ny31RlRKsnm5RX737iY886UV4cCE8ew5sfY9TMTNE+tmsHX7jvGbRoTOhY8vwRvrjMr/PJzYpbATey+aUjKecz7hxB9u7sXG7sJaiOVb2EnKS4m1R2lt649hfks3bnrR/jyz82XHbukcwq9eb+HnLrYCcTMyGHKIjqueW4nZS9PDr38zfwPufn+z7Rji2pUQrrcImy+78ui5PlLhxLJqeCyZHOd8vt1brxUaTmxGTTgUI3aPq0yDY33QD1U3ouCyVfrOBxaJMaE+CJ9n+IWdnv5yFx76pBU9kRR+9XqLa1/pmKTywpfF3qfBuMRlzqZqf1q9G8DIOf1Kcw2qXSJjhouq6fjZS2swb2VH7g+7wOZ6TdCL8XXBkufEqprO16a3WrrxwCfbcdlTy3k0w6r2MM548DMut7KuJcMptLZrMIH3Nhr7aE9UQigh24oIUjjxMLAprsJD41bMhSuQuTyxwvtJwfPHFCOb0CIbnrlqe2yCgwAAIABJREFU05JmeAqtYzstj85QXDdUwWLNhuLcUBOylTjv1mIn6PfmX504Q4scUTl2e5193vLE6lwRjSStEMop42pRE/BxZYfldTKFgglRScUI/3QuHuJiGZdVLgw1CUoFYBfMwkkFPnM1ZQui3+fF1w5sQsgs1sM8FUy5doYTs0VADOEVc2KZkDhkep4n1hthO865p+rI6IllfQ5zCY9ij1y3QjDsnojKmHg8JuzwolSO+SdWJwYMhT8mqVxJG4jLrvmwgHX9nAUpnPc5XxKyir99ut1237kSm5DhgbExZ8qJZQIGazguTmvV8UzEheuWND1UKUVDld/Llfbt/XE0VQdsrQ+Yp0989moCXtQGfDYDE3v+2bWLpBR4AL5esPuh6kZocXNNAKGEXFROrDiHfF4Pv5e5fiOckDEQk4ZViMNtHHUBK/R8UmMVVB3ojhQmmPH6BC6RJ26wuc885a+s6cRv396YVWEQC564Fb5h635Tjd8IJ3Z8RvTKxCUrf9v5TKcUDQ1mpVBZ0flx2R72wMJW1yqSTMh2KjNi+OhgwjLyME+sVRDPCidmuXfieUZSCp/P+Qq6MUm17W1yPkqsS07slt4oZn2xk3teYrZ7kV2JFddSWdUwmJCxeyjFe7AzZcSeE8v2VqN9TUrRMs6nGDeuZp5v9y/YhqeWtru+F4rL+HBzb7r3M88WO+x72frU2iqEahpfazvCSdvz7PTELt8Vxsdb+3n0k6Lp3OjoamRQdewcTCCcVFw9VG0DcZ4u4NZih90L57kwWSpXAStAUGJL7Illc742aDzfTMZ4eukupBTNFirJoi6yzXM32H7r9ODycGLBEwsAz69ox6WzvszYjSAf2HnVV/nNNnjGddN1HZ9s6y+44CKLsFveHsLHW/uxtTc9eieaUvheV8x9klUNF8/8Ei+u6gRgGA41Pb1oZ3sogcn71Ljm+G/pjea9l23sjqTtj4NxCZKqZ1x3csGV2IAPE+qCRXt0MyHuAywVQlY1fLptAADQ2h9DTFK5wYWnVQzjuXlhZQd+966xN23uiZq/Z/xuwOfJ6XXPpeTu1UqsvT+m+O90JZYpkLlyxMQLHpfUtCqQ4kKUMHMpmJCQlDWIP+98wNiDkS0fRnVYi9k4jLGl54a5tdgpqLBThpzYmEPIch7X2XNPMoUtSdXRG5O4h5PBvFfsMMxLzrwscUlFb1RC15DdcuWsxGwVdrI8gz6vxyYUhJMKr6TL5kjA60F1gAlxGleeWSVTv9djM0Cw0B9blUbVCg1m42JKLDuemxKZKSc2k9fGifi+m1ATl1R4kB5+4wyNZ+Nwzj9F0+D1WD3q6oJ+Hk7M2Kc2gyc26F7CP8Q9sYVZ6Za2hTB76S4ujAKWEjuYkLmCKYZR8vMQqr66VYN0PhPs+o8zz40V+Kk2FUrAUGKbHecuhhMbues6qgO+tErNEe7F83Olsjbo42HZXo+Hz6e6Kh+aawMYTMj8e4UIGuIc8XksT2yu30jIGnQMr8WKCItQYOcFGDmxANAZLswq7Swe5/y3E2dOLFuzXYsJySr+7+NWm0XZLbeHPW+N1QHUBNzDiblRQxY9sekeHWbwSamaLedSUTW8uraTV5FcvivEBTGr5YldcLb1cRaE66bqAOqrfLwoUMKMEgBg26cA4/omZKtPZT55c5pp6BE/Kwl7QSbPiFtO7OVPr8Ajn+1wVfy5EpthXoqCraLpvK4Ae+6tqvmCEit8h0VouBkQdV3PyxObUrS0vsGMV9d24rY3N+C7s77ke4im63zcufLI2HGd66om9GG27zX2/syrO6woMMsTazdMiwZadj3cqvxLqsaFVzfhPpSwPNpuLXbYuJyGGLZH7BxMjzRwwsPvS+yJZakUB+9Tg5qAD6qmY0V7GA9+uh1f7hx0N2wV2MObzUFnsc/0cGJjfWjpikBSdazZXZiXX8RSYn1orPZzxXtjTxT/9VoLlu50zwXPBIuwG4jZC9mJxCSrNkQx94nVv2jtN+4J24PFeR1OyAgnFXyl2bhfYiXk9lAClz+9Ap+1Ggrd8l0h3PWPza5zayAu4cfPruQeRkaPGf7rFoK7tG0wp4ecrfs1fkOGcKtjsr4rglfXdGb9nUyIkTPhpMLDqmVuCDSuFbvfmdqQFkJPVDLbPGrY7OjTXB/058zvf3jRjqzv79VKbFJW4XMJX7D1T2OeWHMRzHXBbQUJBE8smxSiB4jlGzHPSlJRbYu3c0O3KvtmXrC5h1M4n7jL2NmmIbag4dWJ82yxo+t6xurETHjIVJ04rRWRWYgKMFomsP5kjDpBqOXnqur8oQwLHlm3cTjPsVFQkuuFgkW6riOckLlSyRbAgM/D75NYHVQMJ7Z7YmXbuNg5ck+sIOT0xyVMrE9XYiWuxJoeGIdCJ3rysiHObbe8WFZdsTbgcy3sxL5veWLTw4mZ0gMYClVMUm0KWUYlloUTO8KLwkXmxDLrsygssXvAvaS1AahauncqJNwrN2HLacCKSSp8Xo+lXCgqPwZTXAcTcloodV3AKuzEnvHagNlnUBgTG19jlZ/3ka0J2J+DWqHIU3O10xNrjHdTdxSberL3RxTP1x5OnH29yyRgFosVTmydJ1OSCq1QbFWKt84ha06s43ljc9BNEF3ZEcac5e34YJOVHuKmsPTGjEJZzdV+VLuEGw7EJExuruFj4zmxjjA4SbVSFyRFLOykYmVHGNGUiv64jE3dUdw4bw0+325Z2YH0Vg2KpuNA0zjQ0hWFrusIJWQ0mp5YwGodxHJirX3KHqp6gNkOIp+8ObaWiJ8VC/9k8lI5c2LtBRDTDaZWnqzx3kBcslW3dSqnTAljXj1L0RNSTjQtrZiaWHNCPHa2asr891Sd57M7YftLVySFbaZQLhpaM3li39/Ui1BC5ufnNFrOWdaOMx/8DL9+Y72tkJaiara1/7PtA7bzEY/p1rKNGQpdCzupGl9/nOsqMxyKxmzAfn/ZGunMp2V7WVxWc3qsLE9sbmFc0XQ8vGh7WmFNN1hv7qP3b+BGbjY3YhnanBXqiVW4ISpHOLH53Lb2Gfnmazvt6WiFwO5tQ5UfBzZVc88ck6eyFWFzg6V2cCORYx7IqpGexVJnivLEmnsVmzpNpgwpytG8MnFzNb9fbE1i0QDMMPn7f2zG6+u60ObSi3soaZyPs1YDi05wPp+6ruOW11vw85fXZt2D2HvVAS/PcXYyb2UH/rqwNeNvZGPI8XuHTaxD0O8VolksJVbPYPAqFJbXm5BVbO6xe+Drq3xZ21sChiMgG3u1EhuXNe6REx8at7Ya+YTyAnZFQ1SamPI64MiBqg36UCV6Yl361TKY4JKtOrFVSMo6H7fWPE7rdULW+PtVeXpiZdWq8Jgm4AthxuJxM/aJVawqaEyYEnFVYjXLEzskKCpu42DHZgqXWCm3odqyBiXMVkMTHNViAz6vLZwukqbE2sOJmcddVBpVTeeCoBj6Jqs6JtRV8evAYA93Rk+scO+yhcDYLZHunti6oFFgTFzwnZ4splis6hjCb9/eaOVfazr8XmspqQ8ai694jpnCiesc4cS90RReXr2bbwaajoJCVdlzlim0MWjmqwLpuUm5hAu3cOI6RySFkRNreWIBI6dThF1HMZ+5JuBFY5XftchXQ3WAV6CsdTwHTOGqq/KjucZvWpbN8Zr/+NGzK/CjZ1ZkfabFOeL3euD3serE2a99JgETMObyI5/t4L+9azCBdzZ0I5yQ8YPZy2yKtaxqeObLXfx5Ec9zv4YqeD1Gdc9MhBMy/vejra7zV3wtWxE0Hk5sHpt9lgm+j3/exouTsLGIBYrcNvqOUAL7NVbD7/OaOXP2yJCkouErzWbxFCEn1tkPPKloaKiyIiHEomCfmKFgKUXjCg8TrpgS5vTEyqqOwyfW4bAJdXhnQzeSilGTwMiJNeYUU+yqhZxY4zftSkwhnlg3o4eoULqtT7puVZVn6+MGIZd0yKVCMvsc27dfWNGBm15a45pbK2saF7QG4jKiKYULtqJxU1F1vm8w5VNH+pwSx5HNgyGpGnqj9lD86+auwkurdtvmUtIxD8VxiYTiMm6fvwHvbOjJ6IntCCeh6sBHW/qwerel1MtmiDTD1gNbtiuAvNq9sF5antj0cUmqnlGJZQbGuGDUBeAw5psKeYacWABoyxFSbOXE5p6j2/pieHLJLixqHbC9rus6Hlm03VbIrqUzgoP2qUFTTYAbONi1iMmWEivYeAtWANk64HyGmeIR9Bs/zpTYLnPvXLO7eCV2a18MPg8wubkGX2mu4conO2ahObJsrWDrpXMesOeUFTEsRmkSZSefx4h+Aay15o8fbMGd7xhhrZP3qbH2bXM9Y7I5G+shZs/7ZS7Vr9mxBhz3hHli0/ppp4y2NjsGEnjAVEB1Xcfv3t2ELwWvthhO3OCIzGJ0R1JIuuT65kPUPDcmyx4+sQ4BnzetrkAoYRgw2RGc51OITNZvGlDjkpruia3y2+7b1t4Y3tlgr96dKfWLsVcrsQnJ6s+YsHlirX/znFjmBdXcm6MzbCG7Qo6rlRMr2d6vCfh44YykYoX01gV9aRsgE1yy5cMwQdspLDnPiyl3cRdlN9+c2KSLx5r/lrDIufWlVVQjh5CHEama7Tcaquzeq3pHeLFxTCusl13ftGsmjCNhhnf7vR4u/ANmSIN5bZj3ZUK9XYn1ez2CEqvx/ogsT00s7KTrOl8QxdAhRdMR8HkR8HnSPNRunlgmaGXKiXWrxumG+Dm3UCbmia32G1YxK6/anEtcuDfOdVFrP97d0IMuM0dR1XSu9ACGQtUXk2z5pJkLO1len+W7QrjoiaX44wdb8eKq3fwzheTFMg+4sx8no0pQYp05Q2wONWcYa3rxMsUWSZEyDTFVAS/2rQ/i5m99Ff/zr1Px67MOs30v6Pci6PMgJil83almnljh/vBQ1Co/b8NUm8ETWxf0pXm7nUrrotZ+1/MyzsXuiW00n7/+HHlVbPxuntj1XRHM+mInrx766ppO/H9vb8K6zgha++M8xBAwKjo+8Ml2fLSlDzUBry2HOODzYmJ9FTqHkohLalobKgBY3h7GCyt3o6XLUm6sqBRBic2iaKXM4klOQwF7tl5Y0YEPTc9rhxnazHKnAfd1eXc4yT2ezuqlrArsIWYroYSQEwsAb63vxoebe6FoxjPZYAploic2paj4fPsAjyhilu4+h2fM6cVRNA1+rwfnTdsX6zojWGd6bQxPrF1RY97/Kkc4Mbvn+xfgibWK9FjnKa5rboYkcR4zJWmx4Clk43ALJ2b3fiipQFZ1LuA7c13FAm67Qgn+PfY5VTcUVmZ0Ez/vrIAcs43Dfb5pum4WftT5OWu6jjW7h7ClN2bbx9yqA7ut90zYi5kedHbeIuL8s90DTeP3xu+170/O47P3xL0kWzixrGjYaHZliJspFOycediiw+PjGk7szIlNyHztFfNiFVXDZU8ts/XwtaoT51aOuILiCN8NJxXMWrILH5uGLF3XsbZzCMcc0ADAek7EUHNWa4UpVOycC1FArHBid0+s2GJHZH1XpOgWaZt6opgyvhbVAR8mN9dgKKkgnJAhO3LNP9zci2ueX5U1TFYWvPzMq+n0RjL5msldD3yyHSfe90lB10mcM0G/l0e47BpMoG0gjpdXd/JCi5Maq/ncWdMxhAVb+izHgzmv9zXrlLi1cOJpGo57wtZMpzGHKbc+rwdLzf0wpWiY39KNj7dY+zJ77muDPjMXWUmTxbsjKah6boeaG8wQ9NVxhoJ+2IQ6VPm8QiE84/1QQnasQdb9uvPtjfjNmxvyOp6uW2trf1xGeyiJwyfW8ffrquzhxHOWt+PeD7bafiNXbvfercTKKg/7S2ZQCJyFnYDsIY7ihpyQrGrDg3yxtjaLhKA8sPdsSqwzNNZRFMkNJmi7KbF2T6xi+03AEgbyzYkVFf9sVYFtXmHBoy1aepyWOWfhJWcYJTsma8XCFh63a8YEvJisQtUNhatO8MSKIQ1DppXUmRPr93lR5beqSDs9sWJhp0hK4dfPFrJleoGDfq/r+ab1aXXk6KV7Yt0NBU6SNiU2XdBkeW9izq9xPLsni42DCR4sR1HRdPgchYvY+THFP3NOrFUU5ukvd6GxOoCGKr9NgCgkpJhtRBk9sT4vH4tTYGYKMPOMiXg96Z5YpvyLxo2koqHa74XH48EVJ07Gd47ez3XusgrO2SyvTGirrxbCiZ2eWHP+scJOIuy6sTwj0TDgxKnETtu/HoDhacgGVxJcrjeLGBFTF3SA51O5rVG7BhO2ysSMcbUBDMRlvLa201VoYsqp2JLAmdPt/LeTlKIh6PfaQuMBK+80nFT4+bLQM93xOScd4STP6a0O2KtPs6qqU/c1rnVcNuYDUyIfXrQDzy5r5+sA88QmFHsV455oCgeZQgmzdPc7PGPpSqwOn9eDc47cFwDw+lqjKm9TTYDPKRYWyZ570VgDiOHEuT2xkqLhk239tkqzVkXb7OHE4vPP7ulKIWeTPSc2ox4TyswxMkNch2l0EI8pqw4ldjBh5YGyli8quwf2awO4eJWEdTmT0iSu5SzqJJZSzW4CKpKKyqtRO6sDA+4GE2YAS8gqv+9O41JC1ngOP2DdU0XTudA6vi5oV2IdfWLdCi3xcGIznSLo9/Lxd4STRrFEj3GtXlvTiUtmfglF1fj9ZmHZbuHEmQxloYRshEP6PLYKxaGEjG19cfxt0Q4eFh3nObG5PbFcaXQ8M2xvYe93RVIYiMs4en+jpyfz7PXxUHNFaPdirWmZCiwydN0eZs7mf3phJ+P1Kp4TK6ZgGG3fNrsUUMqHTT1RHGmuS5P3MZTB9lAiTdl5q6Uba3YP4aaX1rpWHAbsezH3UmfwyDPFkRklClHURGUo6PPioHHGuLcPxPH8ig4EfR48+v3puO+io1EdsCKo/v55G+56b3OaJ5b93rJd4TRFkj0LzjnC7pvzuWc594eMr+XGcnYNxLoKCdlIUQr4vKgP+tLaD+q6zhVi5xxa0R7CPe9vcQ2tV1QNDyxsxRZzfzh0grFfHD6xHkG/5YkVw4kTYnSqIOuv74pgwZa+vCpgDyUVvtayPfMIQYmtD/psa1nnUDKtovRgnDyxGUnIKsbxcGJ3qwMTXG2hSlmsW+meWFOJFW4E+3fcoTwkZdET60/zKlotdjI/2JYnNt3rKm7cYjgxKxrDfj/o96QJ7G6wDcHrSVc04pIljNm9veY5CJZf43X7A+m0Kvq8Hh7mFxBCHZnHLVM4cVwoFmB4YjX4PA5PrJDYHmKeWGdOrKl8smNEk04l1vLEiiEmNk+sqsHv8yDo86YpnY3VAVT5vba5xe5zXSZPbAZDgZOErPGNzi3PJy4zZcweXsOOz62DAabEGufOchQV1e6JrReuLQvJGZdBia0ylYZt/XF8vn0QFxy7P47ev8H2mUKKO7FnK5MSK1YOdgrM7O8DHeG/QZ+xqaQVdmLhxMJ1SykqFwyzUWfmu1jVLQ1PbFxWsaU3irv+sRmDCaPoj9/c1IB0TyybG3VVmZVY9px+uTOU0dghPqM+jwf71AYxqama53xlgrcbcTGOsPlj9Yw2Pru0zbBE24qume/pcE8dYJWX+6ISdACf7zAU4Y+29OGz1gH+/V5bz0pmhBGMdy7PCctxl0wBPOD1Ot5XBSOk8X23vrVua89AXBY8sfac851OJdb0xLK1J5SQEZNU7s1jUQviXhJNGX2iDzYFTebddoZ3Oj0GsmpEhezbUIV964M8X7RJyIm1womdObF2z1g+ObEfbenDf73Wgi2mUK1oupVrKaxrz63owD3vb7F9l81jn8f6bG8kxfetbJ5YXvPB/D/zftvXWSOcmBmvdg4m0vrEsjE4vdSASwX+HG2X2DEZTCjlhljZiOhocsgmolfXTQZhikBMUvmz51wHE7KK/RqqrHZoVVb7NnbO42oDrkUvnd5t98JOhoe/ocrPU4JYxM6kpmrEJRW7QglEUgpCSYWvubp5nmzc9j6x7p7YkFlvoLkmYFOoRUPgfLNlUiGeWN7SxrE/sH2dXQe2NjJPbG3QeD6s8EkrRarJ9MSyXTJb6sqi1gGc99gSPL/C6LSQyTOcqbATAJwwuRkAXBWNXN7ZvpiE3qiEqfsZ58U9mqGkEKaump5oI5x652ACS9rc+/W6RUU55RV2PfZrqLK9Xkj7FVEurvIb6Rv7N1RhY3cUb7V045wj98Xxk5tx+iHjAYBHQLaHEhhKKjxfliuxQnqbMy/TWlfdw4mdRib2+mET6jCUVGytbrocSiwbV4MQpcYIJWR+D0SDTFJW8bt3NuGVNZ24/OnlGIxLeOLzNvzilbX4aHMv1nZG8Myydry82igIde5R++GsIyZg8j41CNo8seaznZBdawwAxvzQAbyTo/0c+6zz3wea8wkw5mxS0XDJzKV4e303OsNJW0XphJy7Ty0psbwaWv6e2GxKpN0Ta1UnFpW0wbgE1dzEjXDE9D6xdVWZPbHZlGi22YoCt5snNiYoseNr7bkDAZ8X+egNbHLVV/ldit5YFXdtLS74WOxlyJ0CkDMnFrCEWybIS6pVYCljOLGkcCWWVSf2+7w2QbleCGlgvzM+LSfWHk48lFJsIcZiTuxAwnpwmSdW1412OX6vqcQ6FnGjeI/XVVDhSmye3m4nCVlFQ5UftQGfaz5OQnIaU0xPrDkJ2DxM98SaSqwZmsgQvdynfnUcJtYH8RVh4RLxeIyqz6wf54XH7I+jD3AqsYXnxGYqcW8PJ86uxLLPBf1e+DyedEON7OKJlTUedpmNuqAfcUnl1Un3q6/ixcbeaunB6+u6sK5ziAsmgUw5sdwT63dVYjUzZP+o/eqh6cDGHnel9P+R9+bxll1Vuei3+rV2f/qqc6qvSlUqlUpSaQlpJKGTCCIgCY3SiCgi6lXApz55+pQn4r3g9aKigSv4FKMXpJUgII0EAgkxbaWppCqVqkp1p93n7HZ1e70/5hxzzTXX2uecijT3/Rj/VJ1z9l7tbMYY3ze+kVEn5u9y/8aqoJkOs96QejU6P5Cvv6easp6yCZOpgTrAkHxqbwUwOukDJ5fxf3z2EfyXTx0Uxy5CMIadh+zP7ziKt378QSHKJSdkAPZeyYGk85wqqM9VAxZK8sh0Yl9a459u9jFassQ6udgNkSBde+h8dFxaE2VnlhDBrRyJpcBCILHRcCSW5uyW0ZJwsuqcCQGkCAE5VSLJFWYduJm6Bw2rI7F0flk9vqjv+SNnWvikorxJe2iJl31QucY0D57pnuUMvoos0HpLAjXyOYlOPFN3MVV18NRiVzhRIojlP6v1wsBw8UJgeBAr7+E0Zsl57kcx+uFAoHcqEqsNOS7tXfIaX0Qn9ixDjDFiwkTxAD1Op2941upIbECJY3a+0ZIl0dcT2IaOimMKxgtdz1jZRieIM1RiOZiT/68KZAJsjZERmuVeiJGSlRGmAdS2V9nAe31ILN/HOwH++N+ewO9/4TH2czdb73jw9AocUxf0yBydOExp3bQ2kz7Car1iKah5/9eO4ODpFYSDBBqIEp9H44lO7Jq6WLsJPZUDgDMrfbzu7+/Fs//7N3OqurJR/fKeSXZf03UXGhhDQUZiTzT7aPZC3HJgBq6p494C2i0AwZaTTZ0zy0OC2LWEVGWTnw3tydtGS7jjyAL60UCwTshkBiSQso5oXMvnltFSIEUmVXScWBXq/JyXgtgELCFD65+KxNI4Kmo/mP1sloZ7asXHz16+CYvdEI/PdfCFR2dx59ElvOv2xwQjYaUfwTI0XLm1gT9+yQWcGajlNASavTBzfPIBu0Es1tR/eThbu1pkmSCWPwOZ6Ub3eKLZx51HF8X90TxdC4UFfoSD2CgeIIgT1F0TGlK6EVAs7CQPpNUyWeEgEUihLOwk22I3zXJ4djZ4iJJVamJJdXSV8xfRgOl7qjoxq0lNgzza8BxDPycktmIbmftkrRkGGOfUkKK6WyCbGFCDjuIglv2OJvkyV4ij/wN5OnE3iFFzTTimLpBxU0J1AXCJcebw0zOg2oy0TkgXSETAkZuqY4p2J3IQSxNvrGznVHZNXYdVQCeuuiZcidYByDWx1B8y+96L6JhFRtm9umcOR2IVlewkSYVlhOy7GsQSEquqE9vZIPb2X3xWpqVR7vr4td98YBrTdTePxK6DUvSeLz+B3/7co2sisbahMyqRqecEA5q9EFXHFNe6ndORHNOAaWi5ucXoxKbYMPu8JnY9SGyJU67JqZ+qOaK+msQvnpjriHlAa8rQmtgCJDYesAA2AXDFlhEAw+nB3SAdW+QI7dtYw2w7EBuzbB+56zgePLUi1asV0EBJBEQEsdl3klFQl/6vBupAisTS2Lv7WBO/xetyNtYcscZk6MQFQWw3zK+dJ5s9nF7x4ccDwQyQzQ8HwlnphQwtXeqFguItWk9FTBzppR++G3ccWRDUVco807pFa9TxZg9bRjwYPBlGCM6YJILWCVJHuCqQ2PQeKQDaMpJNEi1IQQWQdSYAzgrh97lV+m7dSxE0EcSa1GInSyeebwewDA11z2SK5KsgsfTe5CQD7alFdZQZgS6eTPMsXQgptfxI0LQJoWNU3CxqqQr6UBCbabHD6cTjZRvTNQdPSWqktGdSQq+yLjox+9nQVglipd9TAmElg8TGAr3zxT2l11Dk3NPe1ZTmoroO9qNBJoiVkdh+GMO1dJFgS79DQTRHtYWwEzv2TN3L0NctQ8OGuouZupe5LxIvPM3XvOUhQaxjMv/jy4fm8JmHTgs/KB4k4v0mSYKlXoiGx0Tv5OdZBDh0RA35+pHYpW6Ie59eFpRcSh4FIohtYc9kRSQY3aKaWP7MGmJPKYl7X+v8AFuv40GCSR7cyS2ZKHCn/UfTNJGUoPVAnkefe/gsHj3bRoJsLb9qxObYPVERx5+qOjjR7Il50/ZjPMiFwS7dXMfFMzX8x4l7AEkFAAAgAElEQVTlwuMV6SWo/gq9+8nvERJL6PTWUQ9xwtboSzfXM5+n5BwZJQ/kbgb0GXUe0fxrKwrUc0ORWB911xT31+yHSv0pza2B2CeKkFg14KXfffSuE7jxvHG86AIWqLPuEBG2jngI4gT/676T4nuy3wqAI7FZ1pR8TfQsgHRsb+Xo+1rzSd6PSSF/upYGsbKv+J2nlkRXlJ4IYtfuk/sjG8SSY14qUGUtQmI7QSScWxURky2KB6KIvzskiF2SgtiSbYhB60cKnVgZIOQMxsnw/o1F51NFKoA0wx8nKXU2pROvryaWNpeyYxYKMaQKv+nG0wki4TzJdKR8UJcPegjho+clD3DaFHKKzjzQKPF6tDSIlenEac3pskInpuvKIrExWv1IBB3s7ymdmFQEt4964rro+ViCTpxdFOuuyTLKct3TOSCxq6lg9kLWu7TmFvcd64UxT6akSEtRq6eyGKfsXKeW5SA2XUqKnu1q9mcv348/e/mFeMeNTACJkFgKptaDxD54agXfPb4kNsNhqJBco6tSuhY6AUZLqToroVuOoQkkdpAkeOdnHsaHvn1MqIsL5z5ktWzOOoLYsm2g5Uc4s+Kj4VnwLEMED0d5EBsNErGRUbZ9WE1smdORZRBRpixtbriYrjl45MywIDYdF1TffCFPJhxUvtP2I/zlN5/C5w6eEe+miDKmttFSA8hMrar0voYFsb1wgLMtnwnPhDF0DTh/ssKSkITESsHaevvEtoNYBIu2URDERoO0v2EQi+TNvo2sFo6o8qRafmq5j0OzbUFdnZFqYtk1sOs6sdQTDIWSZYh1YxgSSwEUOdO6lt7vaMnKlGAsdEPeVzS7Jolnw0XmgHScAxA16YAUxCo1sRTUzHd8jJdtaLw8g+ZcwgWKZKO5JgfThJipyTn583StQDou6LqoFldOzKWtyxQqO9GJufMeKuvsQifAeMXBeMXJ1FfS86NroHct08lVh5z20dGyvQqdOF8TmyKxrLa+4rD5rKoDV11z1ZpYOUBSkVhKaNL+lu4tjEXimgZKdrafcarUnGT6zNL5putOpsWOber47zdfjN99wXnsepTEMO0bLIjNUiXpmqI4wacePI3b7j2ZuRa6n27IKNMNj5XhyM+Z1qK61HWgqzzD1Uym4J9e7ovnLyOxEW8bdKHEGiop87sjzV3S+NjG5xrd66HZNt75mYcziJ58LzRHdnHxt0yCRUFigXSNIAqwfL93HFnA/o1V6NpwwTGAJXrGynaGnrx5xGM1sYLhEOHBUyso2wZ2jJVw2eYGDs93RM9p2Yr2BpWJttwLuaKwmdk/VxMxVU2tiQXS5/2sbSNivSMbxpiSa2LHFN+YTH5HS5IOB92X+nzn2gEmKo5gVyz3wkzZwazUhiZFYvPtB4uC2L+44ygSAL/2YzuE79XxI3SCGM/ePoqaa2ZQVRUgksvYZHXiojWAkpC7OPtgtWQMkF3vKcCvuZa4BnnPkn1TGrdrKRMD/xsEsR/72Mdw4403Yv/+/Xj5y1+Oe+655/t+zn4Y40uHGJ3C5XReVfmPsuwxVxDshemAXm1ihXGCsm3A0AiJzX92sRuk/RCtlI7YV4Wd1FobabMclqEqRGIFDTmBY+rQNVYnSxOONpdU2CmPOhUZOTNVx8ygZTThJ5S6Umr/QVlJyqCX7Xw/rPoqdGJyqmRanUBiFUeZ6hZLtsGSClydWHaUCekMogFW+kxx1hMbEkdipRY7JOwkL/KysNNSN4AG5hzSddH7MjidWG3LUHVMOGZaM3fobBu3/cdJfn1r18SuJhTR40jrhqqDh06tiNoP+TgqEitvCKqwE9kpnlHPqxNLVO0CkR7Vrto2gmdvHxU/j5ZsvPTCDfixnax2ZT1B7JkWEw9JmRPZ8USLJb1DQvZkW+gEfPNm1z9RYf8nJDYaJPjE/afx9cMLuPVOFsSWLbmWeP1I7HTNxclmH6dX+tjAs7N0jXIrGYHEmsV0YrlPrK5pGcQ7GiTCiS87Ji7YUMPDZ1qYa/s5oYoiOvFWjkSrdVVU1yhvUEUIP6FXhNCpiRs5cJXPP6wmFmCtNK7fOYZ33rgTf/vaA7hwYzXTmma+kE6czr1eEGOxG+D9XzuSaUMSxgnafgRHouSRMTpxII5FTviBGQpieUlKNMj05zy94qNkGWIt8yTtg24QY74TCMTEsw1RtzouBbGDJNuzEUjXvdGSLdbpqkTfrLkm4gHrd03X0+yFGQaPzJ4gJNbje5HBaxpTOjFXJzazSaz5diCu1ZFYJHc8uYg33XY/Dp1N1acLg1iFIfQzl28SSNWplT7edNv9eHy2Ld4jXQcFkAKJVRTo2TWme56sjpoisemzmO8Eoq3aRMXOrKV0T7S2b2p4cExd7PPAcHXi0ZI9NFhYjU7cCxmd2LMYa0RVB645ZkbFl54P7TXLEqJZJOzkWEY+iI1Zz3fP0lHionNkqtgg7Tt0vTN1V4jUBbzWerziCH9JlOjw8Un3K9fEAukYKUmCNsu9CP0wTQySz0CfrVMQK9OJKYlQshHGLOD0owEMXeM+SHbtu/2RsyLhBKR9i+fagUgoAGlNbBAP8MR8B340wIU8kQXkkT05AUXrF2lELPcjHFvs4mf+7l58/fACnpD6Z8prIbEViLJ8TGrpJfrESvsNrRGUHJODj0fPtnHdzjFetjR8T51vB8J3I5uqOjjb8sU5236Mw3Md7J2qQNc0XLqJoZz3nsyjsTQG5evMI7EMJNI1LbN/DivdK6KFyz4onWsHFzC6nvsSsg3bp1f6TD06iAZibVdb3cjzl8oqhBCeZcBXrnuu7WOiYov9udkLleC0L+6LxhElqFtrILF3HVvC8/ZMYLruivlMtbN1z8Q13Le6Ygurk1b1Zth4SBMv9AyKFMopWUVJlbXaEs53AtFeilDZss1YYxrySXlxvv+/0Ilvv/12/NEf/RHe8pa34NOf/jQOHDiAN7/5zTh1ariK5vfC/u3xOfwxl3Geqjg5JLYfxiKwiZNUAZfqZ2VE7MmFDjpBhH87NIff+NRBQSf2bI785YrNGY2RNkqPO5+OybKf5JRQwbNcAyLTtcTGpQyiouBTrom1eb9CEhEBUqepxRUEDV1DApZRv+3ek/jzO47mpM5PLffxNS41X3FMEax/+dAcfubv7mXHpTY1IWtG/uHvMMl7ykpSHV3NNXOBijrR6DwAUOKTXM76iWL3XB1xlAaxAUsqmIYGQ9fgWazdjaAJxwMs90LUPVNk8kRNrK5lnDhGJ04noGXoqSBEl9GcRjwLLV7EL9OJbWnxNDQ2qSlIDiLmJL/tnx/CHbxP3XqEne4+toRX/M13C2m0VAf1qz+2AwmAWz56D15y611Y6gaiobWKxKo9KoF8ADXX9pmTkFMnzopmPRP73RfuxnN3jwNYW9ipE0S5TUalNtJYlJFYdYFc6IYYK9vi+huejRHPEjWx/TDGX9xxFACja3WDKFMT2+bqou6QDK9sO8ZL6IYxDp5uiRYlhOzLMyFFYovpxHKfWACZIDYeJOK5lG0D+zZWcXrFx01/fZdoE0HWDQdiHaAgjo6tjimqmZIplUX97AQSG2apnek5i5Mww5BYgM298bKNmw/MYLziCIdbILHtQKxVqjpxzWHCWXceXcRt957EfU+zGi4aK0vdEA5XlpbR2H6mJjYStLOLZpjjNiolN0VrEz/CUjfAaNkS1C05MUaJJEJMyrYhNvkxxYFckpx7Q9eEIqYc7FZdUwQJu7lQ1HwnQBgnon0XtXYAUpE5IKUeNiTxtZpriiCFnCpiHNDznOdJH4A5QrQ+Edovo5W0TxWNmTAewNAYkvA7z2fo3QMnV/DgqRUcPNMSeyitg5RUIVqanBCUW7aRdaS9ruVHWOmHmbWUjseC2CydMQ0WOepl6uJ5idZCvDTncwfPYKkboO3HsAwNVccYisTS9VlcWTcepKUsfshoqI7J9oR+OMDJ5R6e5ggx62vOnsm7Pv8o/uBfD/FnnA3wxss2WkodaT+M4Zk6xsrsXdP6zISdGGOnZOmZ+uKMbxQNJCSWnY+SCfPtQPgYAKO32oaWK9GhrT5PJ+Yid3ye0LvqhQNMKol2+uzIKnTikZKFQNLeSBkTWRTt975wCJ/l6tzyuyGj+12QkNiHTmVFnYB8BwU5iKV1efOIB11j7+jrh6XWKpHsfw4YEKJrKSBQsdHwrIzAkC/GkIzEGhjxLLFW0HGp5+11O8c4cp1frx8720I3iDHX9sW7IivxZIpQ/Q4izHcCQY8lcTo5yCZrKePEMfPCls1eKOjzayGx3z2+hOf95bfx2YNnMr8vqok9MFPH+35qX64eFijueNHwLJ7Q4eJqnN3UVhJV8hii9Zg0ELaOekOQWFvqUZ9FYqmsqMvnICDTidPPnW35QhysF7L4oOVHogSF1shZETCa+IkLpjDiWbj5kml2XAUgsrjfSUzJsm1gkGQDZrqfuXY2iC1q2SjbfDsQSXpKYJb5GLVNHY5RHILSmv6/fRD7kY98BC972ctw8803Y+fOnXjXu96FiYkJ3Hbbbd/X89KietvrLsM1O0Yz2U6ADVBypOJBgtsfYajtZTyTQRntJEnwxo/dj3+89yTufXoZdzy5iCAawNJ1lHhjezk4c00dIyUbi900y0GKdq6pZ4Wd7DRgSpKEbY4cVQTYQnvn0UW84IPfxt/f87Q4hxrEyj1JqV6lbBuCbgBIQawfwTTSHo1xwsQF/vbuE/j0Q9kF4+/veRqff2QWnsXqJaJBgpPLPbz7i4+n9CFJ2OmrT8zjb+8+ASB1SFf4BlZTqMMaioMfunea5ORYyg4nLS6zLR//7auH0fY5EmsZUk0sz3TZLFilTTfgbTRqriUcvBSJZRsyiWq0eE0smamnwj9nWz5GShbqniWK+MkRMw0tE8SOlOw0UOG0qP/61cOIBmkrhLJtwpCQXrJuGIt7v+vYEo4v9XIqeuweBnAt5nz9xU/vxzU7xnCm5Qu1wUHCNika8/efXM5sCHLvMjJDg1joVHViek+Gls9On4uZ/L2sVROrii4AELUVdC00FunZj3gWnpjr4H1fOyLQSoHE8kV+tGQJupqhM2esG7LWF9SrjbXIYsekBX09dOLtPCPf8iMhZlErGPO04Qg6sRrE8kQKvZsRzxKU4gwSaxu44bwxIW8vBxMAC84mqw40pEEsKYKrAWoaxKbPXaUt0vmBtB6tG8YioCKmClmf04MBJlKlmtxnWK79LfN6/LTF1kBqRZalEzdKFtp+JBzgRzlSSHS3xW4g3l12TYkFEhsn6X3v4c9Sdo7J6Wr1Iyz3IuGYAem61fZj3HrnMQDIiMJQsEoBBhnR1WyDbfr0OdnRrDkmRrgjQ60xqPbwx8+fxM7xEn7rc4/g+FIPA0lkDmC0XNvIovjy/2nMWTzR1uqnQSwFfZahCeefaupkiiQ9cxmJbQkkNqU207s9zNH+jh/lkFhKIlAQKdtt953Eh799LONkdjlKSPPszIqfqYmluTBSsjIIVNk20ppYWr91TVAU6d67QYwj8138wRcfx8//4wM4PN9G2WbMmrVqYl+wZwKnVnz8470nM2O4Hw1EG5B+FOP3bj+ED/AEWk2iEx9d7OIIF0pLBWnYtY6XbdHKjoxqYlUkNuLdAlzLQMk2MUjkxHD6fUJbAZZsM7RUjGehEyCQWGwAGzP0/XElOdPshVjuhRk1bvma2j5rz7HSD8WzXumzoPz4EtvnGtwZzlJw2VpS9ywE8UAkyARjQlp3qK/0at0nyKEmf8OPBji60EHFMYSDDrB1X+ZwdEMWxGpgasHX7hjF7skyK+vphXhyIUVfZQYZvQfX1EUQaxk6to16Qi8BYGPI1LUMc2TraAm7uSCTyxMgAHBkvoOybWDnWCnXBYHO+cZ/YL4sm9fZd+Vyf5b8grbPgAkKnlzLYABNIZ2YlZBN8Xc4XXNzzLFmLxRUWzkJrCbuO0GEP/zi4/CjAd731SMZBL2ITqxpGq7fOZZj17DzSH6YooNBLBbHNFBxzJwydiaI5evcyWba91vtQ73YJTpxKnYmjznyYYqFnbJILKnB93iSJIwTAagQGEIlCmXbwFXbRvClt16Ny4cgsdQnlnxBSjbIInw0h+fabI8kpL9IKPTwfAd/990TWO6FeOTMCraMlGAZmljvPYv1tLckX5iSNun5OBLbC9f0p35oQWwQBHj44YdxzTXXZH5/zTXX4L777vu+npsGIGVUXY6CzrZ8vOv2x7DMKaUAW9z+3++ewOVbGriSw/LNXoi7jy2hF7IFcr4diBfU8pn6F+sJOMgElZ5lYKpi44GTyzgyzxYjOg/LuKZBLyErvTDGe79yGC/44HcQDxJBGesGMd7/tSMYJMCff+NJfItn2lTUiikNppLhtsGFG8I0Oy1a0ISs/QwNpniQiGf03q8cxucfPismVDdgzveXfulqjHgWBgnwIe6Yyeema6WN42cu34QbdjGEjRxfmTpcttmiUbToqMJOS708IhHGCaJ4gN//10P4p/sYom8ZukDGY4lGV+YommVq4vkwx9OEztGYFIllCA1tmC0/zgTaTNhpgDMrfXzn2BKu2joiEOdmP5SQWE2I9AAsM0tOgMuPfWKph2dtHcFnfv5K/NnLL8SmBnMy831i0/ZBRHE8UxDQdaWF8fypKn728k0A2FiV+5TuHCvh+Xsm8JG7TuBzB1PlOfkzZFtGmCP37aeWsNwPC4WdyoqAwLkaHXMtOrG82KpGdNxxCTECWDAbDRL8470ncfB0C/2Qqe6NlSzsnarg7TfsxLU7RvH6KzfjZy/fBFPXxKazse6KNYTGEJCiTeuhE+8YS3ulUW2fXAdOwX8q7JQu9rLRnCBBjxftncRL928EwGtieYBWcUzM1D186FWXAMg7at1ggArV1SrvUqUkUpBCDotlaIUMgFBk7UlELsYL9kziHTfsxOVbGgodfoCZOmtAr6IAQDZwrStBLFBce6PW5E5VHCx2U/TnsbNtkX0G2Puj8SGjGzISC7DxVraZOM6IZ2Gq6oi6PNGfsx9iuR9m+l3TO/3IXcfx70cW8I4bdop6VBlhHy1l75/Gd9VhvaRp7I1lkFhL7A0iiOVrwljZxh+/5AL0wgHuOdEUSSG6R0PXsGWkJBBlIFs3Jc/7usvE4XxeeiHPKwpuSAhHrsOlZy7vh7I6MTkz9LxIwZrYM0CaqKHgfFpqhUVD9vMPn8XnHzmbcTI7AVvnJiSxPtk5pvFdso3M2Kt7Vq7FjmVo2MYd3bpriuToce7Anlru486jS2h4rLZvrT6xL7lwA67fOYYPfusp8Z77IVc5N3URiMgtkqqOKRCxlkTJVfUO6F4Ozbbxu59/FHcfW2LlDpaOMS6yRH5GFCfwOZWR3res6EtrmkofdExDCDYx5H+QmTsimABEkoWMkFha/5YF44DX9UkaIBMCiQ3x0btP4P+6naHPk1UnJ4jY7kc8Qc32ZBWJld8JCd1lgljlncUDRkmnZFLAlZwrdnZ/0zQtM1eoJtY2dUzXXfzpyy5E2Taxsebg2GIXRxe6YixlkFhekiLTwdm4K+HoQhcLnQB/853j+OjdJ3J74ztu3IU/fdmFACASIHQtFb4fqzXEAGszEw0SPD7XxmI3xEQ5myDyLEahT2smQwRxklkzRgpKdAAIEUya2zMNN69O3A/Fui7vn+o+9a+PzuL0io8/uGkPosEAH78vVTKnZzFVdcSYWs1InRhIEXVaj1f61HJNQ9kxcxobQSaIZff8dLMPz9KxseqI+nGAIbUDPobpvTZ7oShfGfGsjCovMQ0dU8/trUvdENM8gOyFsRgfVcWHpuPJPmrFMXFgpoa9U1nhTJsnNegeScNBZtKkwk5MB4HepYrEfuvoIl79t/+B//GNo3jDP9yHUys+XnlgWuxvZZsxT6frLkZLtkh4bay52DFWEgF2isQGmQR2kT0zrt/3wJaWlhDHMcbHxzO/Hxsbw5133rnqdw1DQ6NRWvUzRfaeLzwGTeMtM3QNE2NlpujmWQgT4D1fPYxvcorHeNXB4fkO7jm5gsVuiPfdsEvUpf3Lo3P40qNn8YVfuRYA4CeAT7V4QYyxio1wAEDXkEgOYckx8Zsv2ovXf/S7+K9fPYzxio3928ZQ4w3mB5oGiy/g41zZ718em8c/P3Ba0B8bJRtPN/v46pOLOLbUw/tfeRFu/cZR/PqnD+L3X3wBVNBqsubiWLOPVgJAZ8HcSNnGqRUfy3xgbp2qQdOAJGFIYZlvNtWai340wIv2bcBCx8fvc9rSX776AGJNQ8U1sWGiigpfyOZ7Ec6bquBXb9yFd3ziQVy6cxyupSPWNegcXfmdF1+AL3Jp7j6/1nFpwdk8WkIUJ4Xvd4x/rsEzem0+0Dc03Ezw9oUnFvDd4028/uqt+Mqjs7jqvAkcW+5jrhNA03W4toFGo4R6yUY4SDBSY896IRzgsdkWXn3FFjQaJdiSEzI2UkKj4bFNymAZ0vG6J66z7NmIE+DTHLX/xRt24QhvLH26G2EvXyTrFRe2tHi+9xUXoeKYaFQdlF0LZ1oBuuEAYzUXGyaquGmCfc82DeimnnkuQZJgquYKVBAAVqJB7tn50QD1iiN+P01qj7oO02PvbrzhYWSkjA+85lJc/kdfwUNnUzEfcpg2jlfE767fM4H60038yVcYLf+tP7ZTHN9wqTbPekbzlKxRY46sV7JXPU4rZnN2uu7i1HI/QxWul2ycXvGxiQeN9Bz2zjTwqQcZu0C3TIT8nWyeqGB0pIy33MhojS/lz//W7xwXY2HzaEkkDcYbJUzx59Ln+9qoNC6G3luDBRcLnQA7NtTE58nBOLBlBHceWcBko4RGo4QKX8jHG9ljbxr1oWvAzuk6Go0S3nD9Tpxq9vDJB0/Ddi1BTZ6eqKBR91Dm71K3zOxYGgywoexhpBSI+QEAjZINfwDxcxANhPAUHXuq5qIdxLl7Nvla1o8H8CouBgmwcbSEX7x+Bx6e7WCu0xLfiRJgtOLgI2+4AlM1N0fJ3ipR1GfGy+J743xDX+yGomSgB7Y/JNzBpA14ZqyEh8+20OOT5dBcG1bJEfTGOAGqfKxZpg7wJUUz9GxPzB5zuEZGyvj8r1yLmmviY/9xEjB0WC6voQoH6AUxdkvvdpIHdQ+eXsFMw8Uv8jEGADXJGZyeqMCzDCRI0A8HmOfZ7s0banBtA8t9jmzwe7dNHVPjFUxzB+wA74PYIipj1cGFW0dh6hqafoxyla2jlXK6JvzxK/bDlcbEOP+MbeoYG00TLiNlG/1BgpAHJ5snKmg0SihxdDCx01radszWosEgybRgqromQ1g1vp4ZOhyTjblK1YWmpT10Y12DW2LrPa37890QVdfEpsm0HnGkZIt6rU4Qw7NJMTlGoOsYJMBU3cPB0y3otin2IwDo8/EwMVLOMErGKjbOLPtoNEpwl9k91WseLjBNAMcxVnNRcU1E0DDH39EXf+06PDnfwXTdxa13HEU438FvfOYR3HL5Jrxw3wZxbJsH+qN1D6++agu+cWQBD/GAqhfFrL694qDiWoiRDbIm6h7iQYJKlc27MB6gXvdylMfn79uA7x5v4uf/8QEAgMXveaTmYRsXbZvgfoZhmwgSYMKzMcHHleGyueDHAzRKNs6s9BEqPZQdS8f2aUar7wwSxJqGmmvCMNi7dSwD6LG5Ke8fANCJEiz3I1y2dRQPnV5Bh+pH+VyQA62tExXgsTkE0PDoXAebRzx84FUHsGe6hopnI0w66RqVADXPRKVkIxoAOl87Nox4wFNLsNx0P3mcM5fCJF3jjAImiF1yRLASJUAMoOyauTWv5Bgi+O+FMcDLt+TPXb59FJ/gehcvuGAKTy32oFkmfuf2Q5iqOYgBVFwWcPZ47Wqj6uGCTTo+/dAZ/PRH7pFoxs7QvaZkGxjwOeYPWFKm0SjBs03xe7IlTo9+gAuybZmsZP4+StR9xcGk+Q8AY1UH7TDvf/TjBI2Shcm6B8vQsGmsjEfPtjOfW+nHmOJ7W0UKWmLpvQDASpjA0DXcctU2fOKBM3hyqSv+TvvNx950JaYbXk7IqchsngC5bPsY7nhyEXtn6vj0Q2cwMA2EA6BSstEo2ewepOvQTJa8tk0dK/yez3QCbBktocHXzlKF7WMP8zG2d9MIRkbKaJQsdLmwXsUxMVVzseSz/bOv+Go110KANN5Z8SNcvn0U9xxvAqYB8LG6YTTdE2te2vJqSvo9APyvtzw79wyqJRtBnMBweN32ZBVfP7yAuU4ITeOJcj6fm36MjQ0PWzawOd9PsrHY4wsnoWvAL1y3A3/1jSdxwcYaXnLpJrzva0ew3I9Q4XPmnS/ai1/yIzx8ko23DQ0X73zBHpxc6uGtt90Hg+9F7XCAiQLGjWw/tCCWrAipWQu9ieMEzWaeNrmWPXyyiU4QY//GGlxTxzKnIphgNYVxArzjhp3Y1PAQxgN85+gi5vhnGqYGn1/X6WYPSQI8cZLVGC21fbGoLHUCjJcsJp7UD+FLm49jaNhVd/Del+zFXceaeNNVWzDwQzR9hgCsdAO0e7wQmmfQ7j++BNvQ8PlfuAqfeOA0NAAHT63gCU6DedZMDZffchF+7h/uxyfueTpHJ37jFZvxB188hNd86C7smijD0DS8cM8E3vPlJ/CHn38UW0c8bCpbKFms5YehaQj5vSwudbHcCzHmmfg/n7cL//roWbz7S0/gsZNNtHshLE1Ds9lFxDfPxbaPumfhookyvvRLVwNRDM800Gz7SCJG8Wmv9BD4vD6KP1tHWmt+7brtmKjYhe/X4JktnWfmZ5f7MHUNFcXh/fbheZRtA7989Ra87dlbAQCfBNDqhag5rKVSs9mFYzC0NeTZpD/518egAbj5og1oNruwpJqUbsdHEwlsQ8P8CqPh2kjH4SCKEUQDfPaBU7h+5xjKSFDj9ONf/af7ccsBVo/g94MM3c3yVRsAACAASURBVMoexLBi9hx13vJopR/C4tco7l0D2r0g87vlTgjP0jOZ1aNnW7ln1w0iGIP0WhP+vs4udXGW6uDCSPy9YhuYk9BNyppG/RQN8HTgAy+7EP/zO8dxwYYqnrNrTHyfxqBn6c9onpL1eeZ7bqmLz8y2cP3OscK14cmzLegacPnmOj673MdkxRFBbJlnNSscbU+iGM1mFy/eM45Nlf1468cfwlyzC4eHZJ6G4mvmtScAMCHVDiKKsbzcg2PqmFvhjncQreu+t42y1hQ1Iz1n1THhRwH2T1Vw55EFOHyMJVTCEGaPffmWBj735qtQka67w+murXZfZDTjfohmkiBJeM/Bjp85TqsXwtISnDdexmjJEn/zTB1L7b74+dDZNsI4wWjJEk7dRMnCw2fy467FJfVbvRCn5th6pcUDNJtdmEjQ8dN7WekGsEwddUNDv+NDbQCRJAl0TmG3BgPxPY1oR90QeyYrODTbxlNnV3DBmAdfUaat2wZa/QhPL7IA4mSzj0eOLWbOo/FjyyrPy50Acy2GvnaCGCeXeqjYBlsjAPQ6MSxdw0o3wOIyu64mF+/zDE1ca8TXmY4f47zxcuZ5Bfxa922owk0SjJdZ/9j7Tq7gGKcdJn4IiydFK44JjY+JqmOi2ezi8ukabrpgEiN8rJ/l6GDkR2i3+pis2Dg618bCEjte6IfiGrZWmGMv3jt/AJ6ZncNly8B8y8eTvH9wSWfjTksSdP0I9xyeE589vdRDs9nFSj/MMpJMHUhMLKywv7d7IUw9Hb81xxRJg8UVH02+T9D6//RiFyOehV4nHSV11xRBbIszqUY8Cx0/xjGekKvytWCu2cNKJ13flvj/w36AksSGKJtMXLHZ7KLJ53bQCzDBUQ9HY6jRUtuHH0QYLVmo6cAlnM6pDQaYa/l4eqmHUdfEVTNp0L3E78nvBRjnVECiqScJ0+LQ4gFMjZXdyHRGm69Vp+faad/iuVZOGXa6bOGvbr4I7/nyYTx0egVNuucoRllj6LWnMZS01fHR7oXYWLGR8Dl1dr6NmpagH8SYrrk4A+DpuXbmHLahQwtCGLqGp+c76PsR6raBmOY539tdy0As7R+GBhyb76AbxJgo8bZFHPkxC9y/uqWj5po4dGoFR2bb2D1RxkyJjXsMBugHsRg/i20mqIZ4gH4Y4yyf71WeqJxb6mDc1pEkCR7gvU2XO+neKo8NsqOnltOes36EVjeErWu5NY9QRF1j606z7cMysp87f6wk2E27OBK7uNzD42dWsNi2Yek6bF1DoGtoEvrrh5j0SNHVwP989cXwLANBNBi619gGW5OazS6WOj5cPpdNXcv5Eoe4L0t1+RXl3mhMzLeyK7Mn+UBV28DcSj93PQv8fbxk7yR2jXp4Yq7DyjrIH0kY3dbj55S9OZp/ZGebXcYGWelh+6iHrz8xj6WlDjRNwzKfP7EfotNaHwPMNXV4po5LNlSwe6KMnbyH6an5NmMPxgN4poamsl+udHzYho4doyU8eKKJZrOLp+Y62DrqYSDNn7pn4Z4jLMm+qczGa80xMdvsoWSz+vOSpaPZZsfvBjF0yVcr2wYW+DMdJEysr24b0DVgcaWHU/NsPupx+pw8U8cxKisL1/ZFEu67nubHGufj7MRSFx4P1pf59Z1u9rB7ooJeu4+SZeDsUjdz/NNLHTQ8C2+4bAaDKMb1O8aEfwQAJdMQn3cBnOB+Qd0xMe2ZsGKWwFpYZsedXekLhtEw+6HRiUdGRmAYBubm5jK/X1hYyKGz3ysjEZB+FGcy/S/eN4Vrd4zhrdduw80HpnHNjlFBkyAn0DZ0kdkhygQVT7f6kXBwV/ohbFOHpWtc9CbNJtI5r90xhrffsDMjpOGaOnq8JtbQU7Gh2RYLDC1Dx6svnRHU05U+2zgsnuljvRLZNchM3ItnanjLNdtwpuXjbIstqD+5bwqbGi5W+hHeet12mHpKgzF0DTo/QJ/XdDDlXB0v5plkRneKRU0B0Q/bfsQcFMk8S+cCB6wJuqZpwhFr+RGnV6TvYkPNybR8kI2og0QrW+wGqDpmjr45x3tyyQEPq1EeCHVigNEmJqsOpusuLEPDkfkuXnlJ+oxNQxcIJ33HMXWR5crSiRklY7ETCLXPbaMlfPJNV8DUNYEsmNI4ArLy+I6po+NH4plnn6ORo+D0wpgvhOnzU+tDB0kilC7J5P5jQmBMoqmWHSNDC6LN2zUNUfNTsk24loFfvm47bjhvPPOsDV1DyTIExfWZGqEi3ziygHd85pFMewHZzrZ8TFQc3HxgBm+7brtQF6brMHRN0FFprFmGLii9nSAWDrAqqiOuRaITZ/qcWWn7EaoPkWlKqxmdf0o6HtGCrtkxij+86XyhqkhjpmRnx4WmabneeikNO20xQnOGKPEqZa7L+9O95yV78c7n7hK/rzhmpib2EHdiL9vcEL+bqDD6lEpRI9qqLKxDGgAl28i1vlFFq2TTNU3Ul8rUYrlOm0SS5hU6MYVPJA5zdKErNtV7TqRiR0A6PjLCTiHrC0t1QLMtv7BNgVwT2+yF6ASxqPMCsjVYk4qA0C0HZvCmZ23BX99yMUxdw623XIxfv2EnAEYLrjgGTF3D8/dM4Nodo/jt5+2S+gmyfy/YUMX//aLzRe0qBYJE1d1Qc3F6uS9RY4dv/3R/ag12jdOJSQV6IiPslAjl6p3jJTGnmkrdlGcZqDopTT2MshRUmS7eCdISGxofZ1o+xso2dC4cBCDj6EQDpjQ9wlkmNB5GJY0GucUOBYieyXQBaJ+pe6ZUE8v+NXUNW0c81FwTmxseyhz9P7HUE+OPTE4unlQUvgOJ0r2p7haWzzimDtfSsSKVo9DzA1KlUIBR6DtBnE2QGjrOm6jgb15zCXaNlyW1adZi56OvPYDn75mAyUtVqA6X1jRqSRcnyKiqqteoaxrGSpYQEpM1H+i9erz/LNlMwxOMjn0bWHBP6r9F6uSuxVq5HJpt4yTvr1z0nAG2r1UcU4gtpmVTnE4cpkrVNEfUmliqb6WnSarWpq4hiNL6YdXo3VBJQLOgpu+i6TSZsXeKs3j4MVt9Rn13uFK4qInVmQLwzZdM4y9feRF2jJWxseYO9ZUAtg9RGVfHj8S+6Ej162Qnl7PjUy3poPtSayBVOnFRSxSiE28fK+GmC6ZQslmteNq+kpV50Ri7cksDB7jasbpPLXVT2vHuiTKW+5FUPpL66us119QxVraxb0MVH3vdZdhA1PZ+JMTVqsoeCEDQxC+aruHRs6wc6eRyT6iXAykV+rGzbUzXXaH90vBYq8M2L0mr2AbaARMAlXvTAtnOHW0/wiBhCTuP+7NUkifraZS5ujf9fy2j+Upze+sIQ8w7AfPxaX4NkgSzLV9Q++uemaMTL3VDjJQsGLqGN1+9FXv4+Kbxo4o2XrmV9a9/9YGZzOf60QCffegMHp/rYFM9u66q9kMLYm3bxr59+3LU4TvvvBMHDhz4vpyT6GasH1p66y84fxL/7af24Y1XbRHOOG0q5GhZRlrLSLUbtDm2/FQdlQQzLFPn9ZlZYadh5lo6fK5ObGiacHjOtvyMOAhdQ6sfZQa7Z6UCLLITbRu6cPoWOgHrg2jo+O3nnYfXXDaDG3YxJ5kWuPlOIJRmm3yCULBm6Bqv82HBbSqCoovnoA5Sh4tm0cYg38NKn31e3XiHGV2HWFD7bGFW+33NtYNcn1nXYop8pE4MsPqR9//UPuwcL+Mrv/xs/NMbLsPbrtsmXYssUEFBrIEF/t6rmSBWE313ZQd3E6cg07uxeIsdMtlZlh1Ptb9qw7NyGwip48oBqBrE+tEACbLOKKletvqRUHWVA4iybeacFZ2rVtOCV1pDsKnsGKLe6pkajSu6FrWVBdnZlT6mqg72TFbw+is3p+OMJ2fKkmMqO1j0u44fpUHskKyfqWtCxVYWlKHxXrYNgaSsR9gJAF54/gReeuGGzDlpTI2Xbfz43knhKNH4Wy3QI0v767J6MKpDIZOVZMl6YVy44VWcbE3s47NteJYunC8gbVCvttkhFfdukIpYUBDuKkkZ6l+5mtE6lg1i0zE2VrZgG5rYWEMpgahraV306RUf+7kjqfYztcUalTrf850A8SARtULRIMmJ0dFGT840BdD1TJ1z+nzVxMPlWxp4yzXbxNgZrzhCNGVJUu5867Xb8acvuxA37p4Qe0TVyY9Zz0oTUbTebKw5OL3Sz4gUDTNyFIuD2Egk8sjZpSTeUpclV3eNl4XgCV0Hnc6z2NpAa2IgKdqqz6wTRLmaWCCdp5R0aCh1U8v9SNRAzou5narlB3HqLIp9k/8snDTXEqqdQtPAYG1v/uUXrsJP7JviPkWEp5s9bBpRg9j0ek8pQYLscJuGjs0cAZJfiWvqcM1UtZqMxqhcB36MJ/g2SAkxeR2quSnNm/yDvVNVnnBnPc6p9pX2ky5v9QMADb6nqcI9dI6xso35TsDFI/NJWs8yYJtpD2Y5CN07VYFj6qKueKbAafUsAzvHy3jkTAtxAmwdSYM3Vaio5UeocL8iiAdCFyBthcV+JpGksbKNbsi7CMRMKMc1dVy6uS7EcCjIm6w6CHjLpiJ/jp4tjaGiIHZDzRUJte1jJVH33AtZXWI/GsDjezQFMNRV4p3P3SWSaWuZ3HWjE8Si3V2R4Jjadq9I2AnIr/HjUp1zw7MLa2Jb/Ww7wrSfblpfy77PPvOGq7bgN3nPeHWfYgJQbF6fN8H2IEqcrWddU821jEzimvRZlnnNr23oKDtmTheC/NmLpmsI4wTffHIRQZxgU8MVirv0jA/NtoROAbtPiyc5mRJw2THR8WMxLuU1Vw6gKRnY8CwhtEXXVckEsefWHYLGJ83tmmuKMUZt1/xogGOLPfSjgRAOq7t5n3SxG+Zq34HUX1R9jOm6i+++/XoR7NKcOtvy8f98+XEc2FTHL16zddXr/6GqE7/xjW/Epz71KXz84x/HkSNH8O53vxuzs7N41ate9X05X4VqpiQUcZiZWopGAmyTps2Dsh+z3Glt+1Gm+NoydLGAFmVQi8zlPUJTJJbEi8JMUGTp6TXIx3MtXaAu8qJpc6l/gC0AVNd75dYR/Ppzdoqg/Vev3wGAFbjTHkTBunx+WnD70SBFLQxCYvPZSSZWxOgK9PwoOFnuhfAsQ6jQAtkgQzXRJ9ZKlZsrBUjsfCfItL+hZ8J6BqbqxKzlUXrMHWPlzLXIm7ElfYecB1WdmKyuOLiepaPFKdSyOrFlaBkEU35v6uLDsnfZDaLD+7vKzr8qckQbhfpeKnxhlls9kZXt/CZH9yeCqTX6v166qY6LpYzzMzE6p9znWLVBkuBMyxfoOZA6TpahY89kBRdsqKa9Lo3s8za0tAWUBlYDWmSGrglUXhaUIcd6suoIZsZaawvZxTN1/O4Ld2fGQM01C0VQhDNor31sWZit7Ue5jUNV84xEBrgoiDUza9vjs22cN1HJjP39vN6bxOXIyAGJk7QdFm1mJYupCtNnemHx+WUjJ6dI2Alg76IuJXvkBKKpa5lnunWEZcyfnM9SrWgO0jMcLdliTsnvXW1TQG0rVISjSOUXyAexRSYHbXWvOFAF8s3rAfacaf22RBDrYr4TiD1t1SDWpWRDdrzVXEsEsTLDgUR0qE/yqFSjSk4qKbl6lp4ZV2GcZMTuZPS6KyGx8pijgITWUnK2ZBtRg1iBxMUI4wSexZgltAbSscY5Sl73TCQAH6dZB5la43mWgYVuiNl2IAJRMnlfOrvSz/gChDDROUnxWBYpZOrEek6wie5TRmJJrXdKUcsly7ZMys4zy9AQcSTWk1Tqu0HqWNP4I6Qtbf2li+te6FCLHTlhlj4vIJ2voq2TZ2GiYqNkGQyNc83M+xfPwmTMGXqCchBMfS6pJVDHj1B1CYlNUnVyftz3/tth/O7nHxUKrtvHSugGMT74rafwC//0oAjE/+rmi/G6K5gI4qzEPPAjYqLl1ytimhDTYrEbZpIZZJdubmC65qDmWkKAqRvG6HCxRZePZ9rz5Frt9Rr5lADzUUnJvljYqS8Sk/K8JiMfQx6Lhq6hJr2rhsf6F8sMG4AlFeQ1Sh5fgBzEpuek+1WR2GwQy4Kpxzk7KG1btf6w5sX7pvDje9P2O47JfHdaM2yBxCpBbDSAY+giGfr5R5jOy6Z6isRSK8YTzT7Ol5K+ddfEci/knTNSJJZYn/L8lNdJWs/rriXaYNHf5OdbdrK+3FpGz4vmdtk2M73DXQ5cPcRLSC7kzIm6lwc7lrpBIRAg5v8aQbVpsETXqeU+BgnwUxduyCWMc99Z6wa/n3bTTTdhaWkJH/zgBzE7O4vdu3fj1ltvxczMzPflfCXbQCeIOL1y9ZdLTgwtArahY8ADGVpIicbAqAfpZLMMTdCrskHs6khsP0zVc2XnQXZgSEm35WezW65piIxjpg+prokNZ5BgaF+m63aO4Zu/di3iQYIvPsbEiZrKhgVA9LP1pUyk7Aypz5UUd30p2y4jsZsaXmbTWw3F2r+xhhfvm8rQccqc6iybHw3ySKxJC2e07kyd/DlTILF6oSKcvHCqDq5rGaKNhymhmSrqLP+sBrF1z8xI8gPM+SrZpshsbhnxcHypl9lgaUMpKcFP1WE0lSLl4XJBgEr3x65xdeonALz7J/au+vf1mAhiRSsOZUPrhnjt3/0HZtsBnr9nQvxeRvxfd+VmvO7KzXiYL8CyQ6FpGkq2iW7AnNqRkjV0bGSSFJ4lWmLRJjHMeTxXq3sWRst27jpobJQLREfy18o+Gw9Yj0R146CAg0yg8QUbHm3gSZIgAVOefdHeycz4vGrbCM6bKOOf7juFl+7fIIJyOYic43VmlPzwJEem7umZ1gLDrFFizq78fOVN2rMMkeUGsv28TV0XyBzAqG+TFVugP2S0PpKKeM01BYomv+N6QRAbSHTios9ZhgZDY0H9VIECs2qZILYgUKX1V2VtAOz5UubeNlMFyEEiUSNXcYxXoxP70QDHl3qY4JRedm/M4e5zCt5Y2UYvZA4tvY+ZhodTKz5cTvGn/VNFYmVnthumvdb3TFXwzht34iuPz+PZO1inABoLjqmLZMo9J5YBSEEsD0BktXwKVIhKS7RYgAUqrL0UewasjUXa11W2sm2IUpEiOjFZnDAaOiVCKDihvY/RQheyyTCOxKpGzyqDxPJrKErmAVkfQk1MmEbKIvCstDxFRmJp/FEyaqRkocXVVQGGyD98psVq1mWmkcRooOfV4TW2AKOEapoGzzYEZb9oDWZIbIq+ZoJYfg39aIBWP0Kbo450bZQEpfd/crmPbhBjpuHB0IAtDQ9PzLbx1EIXTzd72Dzi5oJvYtnQffaHMEfo88RQWOoGmXdC9vYbdooAxOWsKEo6suS6jn6YR7TPxZhPmSrEExKbS2LyrgrP3b0Zj8+2MS7Na3FfZgoc1F1TMB3kz9F8W+qG8Ors89THNIsUZoPYZQlhVO+3CIm9mAfOFcfEdM3B47MdcR+GhkJq/jB741VbMj9rmoaamwojOaaOCh+zBDDRc7D5Orep4eLbR1kCd6bh4klej+pHA9GObo+CxK702XveM1kWSCztwzkkltTzOYhR90yOxKZ04iIk1uTMybWM9jxCYku2wbtPLMCzWFDpRwMcPL2CqmNii1Bnt3I09KFIrMRYW8tcSxeskXpBQku1HyoSCwCvfe1r8dWvfhUHDx7EJz/5SVxxxRXft3OVLNbEd7kfrtkGo5hOnP0OLWxqVsvSdZg6o7jEg0ScqyhzR0bZsTjJIrGA4ghJSKx8D66V8uAdESQxpE9tBbPaNVCTbCANYmUniXrq9sO4sKeiurA7loF+OOBy5fzzRrrpeJYh7old3/D3UnVN/N6P78kM7IrUp1M2tecmnbsdxOsOYmkhNTSIxVpeFIYhsSoywujEEf+cLt6Beh3Djg3k6cREeyrZuhhXJBM/K9HPirJ7dPyWH4mNRK2JVY2ulZ5JUcDzvTaB8PNrVDe0k8s9zLYDvP7KzXjDlelm5BbUNbIaunwPzhLfoKhH7DCTN0aGNFni+0C2xnG9NbFF9nNXbcG7bzo/9/sxLr62nuee0olZix21NlluhwKkSFRRYqLimIgTNldPNvvoBDH2TGaRWNc0cMuBaRye7+D+kyk9V6bzUsBC10+IbC9M2+94a9zbJTM1Qe8jk59HyTK4g8VVRKXxYhpapnXNSMnCRMXJCeHJa1qZO8KEPsjo6VA6scIWkIMHTdPEXF0PEqtrmngnRWirI/6Wz1SXLENk7mnOEg2eqIOWPnytpWOq4432okOzbWyUkGmiqPu8rpLmx0InENdBdGzqrUwBfxgPBEMIUGpi/QhxktaP3nxgBn99y8W4Zns+iP3gzRfjzc9OqWceHw/kaFVsE56loxcOBGJIz0DeQy7hLBLai/727hP4d961wFSemex4b8nRibOflXtahgpqRK1WJipyMswoZHUUIbGPcKFHtVaUTPYhckisnvZwdC19XUgstb0Q6HXZxlI3RC+MMwFXDol1TKaSS3WNk1Qzx76zqVFcH+xZOnZyDYGGZ2XGCN3nrXcew0/cehdWeJkRPVu6N/k7S70QTy10WV9wh7UcXOFJ3SBK+xbTfKU9daxk8/7bceE6L+Y3f4+9cFCYgGp4VoayuUgtqBJ2bYxZkB5/Nb9tmBGCRvOS/EDHzGoinOHdDbaOepiuuzkqMV0jGQWrY0qw0vDS8gcytY+pfCzad5oSwqjer5y4JmEjueXKjvEyjnEWQhgnGSbdM7WqY2KRkFhDF6CEXP4iJ95u2DUO09Bx+eY6NtbctCY2GuAYp6zvGEsTMJNVJiM52w4EEpsg7TebqYl1DAmJpZaUFq+JjYWujOw3k79eto11tTikOUwtpMq2IZBYl49DFsS2sG9jVfjCqk9KbQpHV0Ni1+G/eJYh2E9FDCTVfujqxD9IIxSAOayVVT+bBrEDQfu0lYVkTqlVIWOUUQ0RpxNXHBP9KFjVubUM1rcyWguJNVIHVQ6K5c/ThkwDW0YG15PRo5pYGqCyk8ToxLFwVoC1kdh5tSZWbjtk6yJYMbT11TPIjgRDYokSrIugTUUy6fl0/Gjd1BwRbCriS2TywpwRJsmhwGnm05RqYlXqtFzbq6IrddcSzdPl/oNy5nzfhhpuf2QWZ1b6mKm7SJJEZPeK6MTL/RSJzdbE5scpzQdRE/uDCGL1bCZdpRPTJnj1tpHM9YgkjvR8N9RcfPrnr8w0pwfSOvmFblC4+KrXAvAglrepojVl8nuExG4Z8XLOMMD6SV6zY2xVuj0ZDW+GxEY5ZF0VdlodiWW/a/UjQSfaM1XBgAd/jsnalT1rGwsqnlrsClGODBJLQaxE3wfY+irqtte4t9dctgmvuWxT5ncyrd3jdOIjvMdolEFitYySN9EY0/tkSR25JrZkZ1FfGVHJ04kNLHbDVZFYuu9OEOeEnYZZyWYtO1R6HzsWX9+LkFhO1wbSNZ9QQCEyt8paS0GzumcRw+XpZj9TLmAZbExRf1NKCC10AjR7IWxDE8/btQwkSSJadwVxgpIt04l5O6myzYSdVql1o72O7rEijQfH1DFZdUTNnMt7oPbCGEHMAhXL0IAwu2+9/KKNePlFG/GZh1gPyo/efUK6z+w1vOlZW7B7ooI4STJoC52f/iX0+rvHm/jWk4u4igua0HXvGmcB2oyUGHAtPRNcf/hVF2PraEmgOzIS+8RcB5ahZa5BXisye7gSGFuGHMQamZrFFInNCjtRIEPXN1Zmmsm+ItKlBoMly0DsJlIQWxa/p/tXEwX0/UbJwmjJyiHe9Azve3pZ/K7imMJfa/mMfaUm6e4/uYyZuouybSCMEyx2AvSjAfwoFs+O7m+OI0M0rlt+VJhgoHPIwkiXbm7kPpe9Nz2jLM2YAQZcKw2anklw5nIAgYIg2tdVOvExiUnws1dsLty/HOleR0s2nlrsYVRJCNOYkO+lVUB3VenEFLzJ4mw0bkJpD2n7UUZkDGDskvtPsveuMjqeqVUcViIAsDlEx2wHkVj3fd5DFgB+9cd24Feu3y4CRrkmlupZ5WuW9SRk7RCaz7KvVnct9MIB5tu+SM42POomEqHVj3IJTtE7fp2aJCKI7YVC92braJpg0TXgeDPEmZU+nsM1dNg9sT2TYpamkuCS7ZyQWFPHiXZf3P9a9kNHYn+QRg9wsRuuiZbQXOhLmUXVgVzsFgexFlegDeIE0WAgApLV6MSmzmhNak0soFLS0mN4BYErkFVgBbIbu7UeJ1hBYqtFSGwBskp/l00WgpJrFdN7MIY+37Wuj91bmq2WB3xePZRdFwlvrccscb1r163Kn1EdXNlBytbEKkGsfIwcEpsKDgBprbZrpcJO+wQS6+P9XzuCX/vkwZT6rByPaKJdEeRK97UKnfhcBIb+s6bSiUMlQOgOCb7SZEn2+W6subnMZJmXGCwNocGQyWPOMVn7DsdMhUrkAGe9NbHnYpahF9LSikzTNBi6xlVa4xyybhtZIZROAaWcjMb4HU8u4L3/dhgzdRc7x8pi43UlRx3IslJkOi+xVgQSK4nHpJT3cx9TMmWqxOnElHyTHSBTZ4lIqtepe5YIJA0NIoGRRWKzaIhMAVaDU9sophOrwWfJNmBow1WwVaNnUrSZ0x6hlk4A2XdJ6+tU1YEGFoACq9OJGy4JO2XHsryubpREhGwzWxNLKM3f3/M0Pn7/KWwdLaXCfBw5IDQyzNGJ2ee2jnoZdeKidVtNWMlj3VbmTMk2mMp7GItz0rwumrNF+5F6DSMlGz+5fwNedtHG3NpCe+H5kxUYGvA//v0oPnLXCTw+18ERXhpC6+n5U1X85Sv343m7084Mrqln9tPJqoOGZwknmZBYGovbR0tinbcMLUP1XA2JNXU9VWi2dCHA1Mkgsdn9hwIOuSZWPLcCoUbaL67aNoJrd4zi4pkaXnPZDK7jPY3ptxzbwAAAIABJREFUmmYUOrGlILlvvnorXn1ZttyMxoCclCJ1YgCCoqv6JovdEJNVR8wx0d/YjyRhN/a3+U4A19SFDzlIipl1NI4mpWu5cssaQayp53xJSriRPZPgjHwvSgLLSKy8Tj/Jk37bx0p42UUbcdMFU7ljyWNmdAgSS8GLXCdZJDxEz5uuS322gITEStfZLKAdb6w5aPtM1TkaJLkk0zOxipO263IMHRU+d+Q2V76kCQNk24JSwO/HA7SDCIaW9c93jpfFdVY4EgukQrHys37+ngkYuoYPf+c4lnshDI0F2a6lo8uRWDVYLZ9DwAik83WxG6LC0dstoiaWjUOqUSUlcSDdk0jsa1GUGuT3NporRaVqwz4LFDOQVPuRCmJp8jAUc/VbJzSyF6b0UzXoUJhowkiBNoxZSxd6cavVfNGmTkGsnPnK0iyyASCZ/OIpQ0SD0zbTBXFYTaxstIekdGJV2GmVmlhl43c5FUEWdpIXGs8yxM/rXajl88k1sfKAzyGx0nUVZXqLjK7LyqBwhvibvMnIn1EdXHmsmXpKS7cUh0heFIuEnYC0LoKQWNfUUbKYEiNl8dt+jONLPRxd7KYS7Mo1VV0TrT4TdvIsPePwlJ38ZvJDoRNT6yaBxGYDBKF4q8wrISC2jg2NWmTIghFFlqETWzo21txMxjGLxH7/n81aZuqaQGLVpASJzpF1lTY8stE4/OA3n0LFMfChV10M29TF7+k7NL9kp0Om81KWOYfEBrFgT6xWbrGaCcE3O6UTD5IkQz+nRBttsA3PEu+s4pgiKKc5ff5UBfs31rKUTF4LDeSTQo6V1v6L3xU4ztTSYb11W3K7F9XUFjuZv0nvUk6aNTwLZ3ivx9XoxOS4DaMTA8jQiS2DtSPrcoVbou1//fACdk9U8Kcvu1CMQ88y4BgpGyBQ0LvLNjdw/c4xXDRdQzRIBOOiKIil9UjUBytIrBzEkmhRP4zFOQVSWDBni/bK1Z5Z7vtmmjyYqjrohqxHPcDqz1Rhvyu2jGSYBQyNk/YE/jdKkj612IVlpC22dk2UUwE7ZR+uyQJjyr2ahoYVUZ+ZJpl6QR6JXe5HGZ0NURMrBbFZJDabJHjz1VvxK9fvgGsZ+PXn7MytIzNKu6EpSQwMAH76kmk8d3eqfyBfg9wmhyGx7PctXn+oMukARvulc9OeutyPcjToThCj5pqZxEZRGRN9XqaFy1TSInMk6r84tlL7/0yCM09BYglMsQ0dfjzAn99xFL/xqYM4utDFaMladf+Tk1kl28BU1cEOzh4go+/LdGIK/OTEvqpOvNAJMF6xM3OB5pm8hhcJQFEi7fRKH0E0OCdl4mFWdUwxlmxTF0HmPz9wGl97Yh4AR32HgC407vyIKWOXnWy7R8vQhbKyjMSe4UkUOfjcPOLhFRdtxKcfPI1Hz7ZRcy1WQ85rYlXRLDomcA5ILL/eubYvEOOGZ2HEs1Bzrcw4JOYEkKLLlDSmmtoiRhu986JSNdVoDTJ4Inkt+5EMYoHVA0og3TADqe/ZelXPqB1PIOjE3NFbJXC2DA3hIBEtdrLOUzHiJ29w8iKjIrEAMhnatYzufbnPaGDZ8xiiRqy4JjaPivkKnVj+fMlOa1fWi8TK369IlD95cVMntvzs1+tArobEVpWFyRSBeL6YPoPE6qnKtXq/9LOGAudRyXKSc+GaOl66fwPefsNOsdi2/QidIMJyLxRZMvV5CHXiIC+ok1F8tdKgnd3fDzCI5eekukW1JpaQWHWhc85hvpZsRqtWe3rmroWPGZ1T3t989VZ84BX7xd/J2dKAQmfpB22mriFOSNgp+3wY9TPNwJEgXNGmR79b7ke4fEtDOGc01mhtoLGbQWK5YA7A6MSyqJlcF7VaTe56LK2zZXTiQcLmgEonBlIEiYSdAJYIk6l2APDrz9mJ337+eeJnqjsahoxSyUAg9fkrqoUrcwdwvVYecj6AUc9ff+VmXLdzLPc3+VnK61fFSQWfVkvymLqGH987icsVKqRMS5X7JQvKnR/BMQ2Mlm3cfMk0fut5u/DXt1yMqaojxiFLXKZsgEBRtN3U8PC+n9onAiNaw4oolan+AyGxCp1YrlW3DLimwYXcBrBNLRdkyVaIxJ7D3JZFj3ZPVnDeRBlvv3EnAObwFSVtM2VBCp2Yxt6WEQ+GrmG2zfqkUzJt13hZfF89tjwW83TitJWL6OdoGeiEsRCVqTqm2Dc9K03OyC120uPlkdi1/C06nirsRHNltQQXnUNGANl8TenErqln9mvyhyarTm7/WO7LSGyWeSXv7UXXdD5/z/IcX6su0TXTfvRknvLu1YT3esw1dSSAoMYSmOKYOuJBgoOnV/Cto4s4eKaVC0hVU5kdH3/j5XjVpVlEnHpZy22YiE4rJ/3EnkKtKjtBDtWldyfv+XTcTBBbpyDWRzhI1u1DrmaZ8jszrYn95IOn8ZuffQSffvA0A2WGrAVyTSy10VGNKMVlCYmlMg+VpfPKA9OIE+DuY0siFqBknNq+iI7J/l3ffirX2NM+o2kaPvDT+/FzV20WQWXDszLJKpWdUUQLJ0trYtcOrOX9cz01vT9SNbHyS12vsBOQOsOGnqpLFpmuMXTWNHRYCQuAY14TC6y+EFs6W1hCjsTqPJBlanDDkNg8Qghk1VnJqo6Jed4ndi0jVK7Zy08QV2rdQDWcmSBWmTikurwanTirfLu2qUgs3btcd1BUs1b0/dWMrkvt5QrkHX76TJVnymRTkdii5yAfu+wYOXXANIhlTgXRvBzLwAUbqrhgQ1Uco+3HaAes7osEKVShq6pjIhokaPbCXEAqLzQuT1oQem2ZrAn8WvPne2Hqe1JrYlMaarGI13o2/pJt4MwKQ6bWg8Q63BlqlCw0pMV6lCsbE231h20GL0+gNkyy2Wa2bQc5qkWbnux8UPYYgKBS0+akaywg6GfoxEzUZLYdYL7tF/YK7IexoMav5egOs5LkeAs0oBsWBrHkLNU9KxOQq0EsGY3zrFpvmFtfqM0H6z/K5m+RKMXbrt+BcxkdNLZrBQkWQ9fwtuu2F36viE4MsLl9eoXRB9daB/+wQGAsQyeup466rDg/XXOhaxre+dxdme/KiHk0SKnXVJ+qGq1LxCYpphNnkyhU+9zjtbkUTNB4Ldk6ukGMQQJULUMcczUVYNnOBemRUcq3XrsdSZIIlIrEe1TLiDWautg7ypLgomXo2Dbq4ch8F1XHFGN+10RZPDN1HAvntOBvlq4JxIzO53Ek9iwXWJmsOnBNXawnoha5IIjNCDtJVP/VrGwbom6aSg8AVqd58HRrVZ0BOseiFDyVbEMkP9t+lOslP1N38dhsG1MSnZhspZ8mGExOOY8HCapKEFtUw/+c88bxnPPGkSQJXnfFpoxq/jArHgeqsNO577fkbxI1lsAUObE4SICjC901Kc+W9BxsUy+8Zk3TuDp8So1e8fMdLkZLFuquKWrV59sBdk2Uc8cydU2sEYvdQNC9VToxAN7/enBOTIlhJu9TjqFn2B17Jiv4qzuPwdCGa1+kpTUx2n5cmBymILYiIbHHFrvQtbwfsnXEE4rQNI8pGdc2I+xUEhC0zp5rTewgybY3o/p6up/zuJI4maxiDshIbJE68frBDxq36xF1An7Eglj5Aa5JJ5Y2KznjYho6YslRo8EFsM1qth3A0jUkhiYW0R1jJUxeOoOrt40MPR9lePthnNbp8CBWdmBkx1ztE0tWhETRgF5PTawu1cSqtDnXNIRTIejEGXpwPjALeA+6IjoxUWGBc0Bi5fuSkFjZwcpdtxJIrsfouor62KrHp2ddxOGX35Oscp2nE/MAuSBbJejEBUisbBXHRCeIRB3piaUeSpaRQzGIgni25eeRWAm5yyOxGkrrVL37z5pK+1aR2E5BPS8gCZutYzyVLUMEx+sLYosXYV3TMFmx0Q0HhX//QZupa2JzUZNnuZpYQmILxp0sMLZbcjRI9dxT1h0/Sil9UZyg7lmYbQeIk+w8kFvspArZz8wBydCJ+TuUHVognfN7N1Tw8JmyEP0BWMKLjqEGLvS+Vdpjvuae3XufJ+tcUy/chC86x97Jq9XErv69/JoFsLktakyfgWNc5jW9CYApCeWUkdhhe6uoieXtQ+IEoldwUcBIATyteYVBLD+XqlXQCwPYpo4pO1u77VkGFjohNI1dcyo8VIDEFtGJz+GZ0TnHK3aaoOTzjlqVqCbPEcc0RHCtJph2jpVxZL6LmmsK5OO88VXoxFLfX3XttgxNdDZIa9eYk3y25aPmmkLFHQAObKqLeSGLSJIvJK+7qrDTMHvlJdO4bHMDOq/nJ3v9lZvx8os35pK6stG9NrsB9m+s4pev247LNjfwLd72pOXH2Fhj9/8nP3kBNjc8fPg7x/DYbDtTu0qm9i32LJYYrrpmZkysdk+apuFXrt+x6j2nx8mPKbUm9hkFsRLCBmRrYgFkkgXb16A80zW1/XjVhELdMzOKta0+R2Klsa5pGvZOVfHoWaaovdAN8Kxy3jemNpVffHQWf/ilxwXLZ0RpleaYOg9ivzc1sbJvJyOxpq7hmh2j+Ohdx7lyffH7F0FsnAxFYq/eNopLZmrYM1kRccLTy32MlvKlJpqmYf90Dd98clHsKZ7FxD2XC4SdKueIxGZESQv2LHqm5ymJBk9KRP+XTx7Et44ugtpDqVayitexIqPvN9ZRDwv8iNGJZYRpvX1igeIaDzK5Fo4y+3KtDcAm42/csDNTJ6EabdB9iddPk0F2YDI1GQrNl0ylWAHpJrYeqiPd+3IvLKTlqq185GAjTydmP7f6kdSDUb4HXThT60Vi5VuQa2IdM134h7XYAdZPCaPrGkYnlo0+U9jPMUMnloSdlM2Ano+K8sjHXeoxlThCYtWNtGwbaPupmMOJZq8wsKYNbbYd5LLkWSQ2i0bbEqXy+215JDZPJy5ZedRa0OnXicSSrRbEmlJiaZhNVp1VN/gfpBm6JoJD9ZpsU88kBAQSW1CvIo/z3RMV5W9GzumW31HI0Qt6dmo7HICEnYrbQK3XKJNdsnSx8cnKrUA6l19x8TRue/1lANK2SxXbzFDtZKMgSUZiDS2/GZdtA3HC1jnb1HH5lgYu5SrN/xlbrSZ2NRuGxMqJimdSP6ZpGqouE8UylT0OYImlYXOA5pdcZxXGg1xNLBnd+/IqSKwqLCZ/T05U0NgjYSdWRpEKsxUFJEVJ1XPpQXneRAWvvnQGz+bK3eza5NKggsDZzO6NtP6qqAohVxXHxLU7RvET+6YwVrYL935geN9fIJvMIAeSVLTPtHyBZh+YqWHfhire9cI9KW1Zul5S5M0KO2mZ4w6zneNlgVrK73mi4mDvVHXV79I9xwl7vpdxCrysTkyfueG8ceyaKAv9iKmqU0hxtDPPJE1kZZDY75GA37BWPVlhp2dAJyZRqnbaOoUdix23JyVcd4ytTieWr3M1P61kmYIhBUDQ1FV/6fypCo4sdFlfXz8uFLqzDA29MMYffPFQjqFApmkaNtYcnF7xRe/n/6zJGgMO1xF454278Kk3XYFRXq7S8qOhz4H8D5+3nCkaX5NVBx961SWYqDhibseDZKjgHyU/yQ+UxbFydOJzrImVa/+LfCBCwLcqXRPkDgOUMPKjQSHAsXuygl3/H3t3HidFfeYP/FNH33Nf3OcAA3I5KBABRVGjIaDgFRCPqIuLcTeecc1Gsya/bDziuShewTMe0cQDEI9gkk0MqxsTNMZFTSSggDLDMDNMz/RMn78/qr/VVd3V3dUzPdM98Hm/Xr4Suqu7a7qrq+v5Ps/3+db4bA2WWFVWZlIcV1wDxHjhka35iiIZT8SGIDYegIlziphz43Mqhnmnsukxdka8xZfPlIkVaXXTOrGGEcI0X2yrcmLjpP5sxMOsviDG903sX7p5usb9Mi9fYZ4Tq2dibZ6ARKkJILoTJ0aF9cYrycF3b8qJRfmWnPreJr8vYpvkkzVg/pxUOXFspGRixQWLRZDoULTufQ9t3YWlD/+vvo6WVSbW2HV4d1vAMigWt7V0BlNKwMX3xFg2LI7huhKXqStpf0oebAhGYnhtexP+9HkbAC0Asgqoc5nDbnx8ppNm8sCSlYa6Egwvsz/fsT+psqQ3bEqZe61IpgZMnfHRdav3SyyhU1viNJVPA8DZjSNMnSyTu16GI9ryZMtnDAMAfRF4IHEBGDB0J+5zObFT1T/DlCDW4juvyhKGlrlR7XOmLcNMbuTkdSiWUwbE9+lAVxAuVcaNpzRg9TFj0Fde/Tcg10ysMYg1Tr/I/TyYrMytmpo6AYBDNfwGpPltHVnhwdozp+H4CdX6sRYMR/X5qcn0IFafw2t9fAJJgbrIOCmynmnT53rGy2T3x9eFVi3WiRXE98ZqySs7nKo2eG08ryjxcufkfRbENCIx996tWl+QihLCMreKr4ytwk2nNkCStGlIWhYveVqPYtloDDD/DonXqyt1YU97APsMQewD35iJx1Y1as+jDx4knk+U6ltd++Qy8Ck+E1myF7yZyuWTulMDWnCQ/L0+fkINTm6oRW1JajkxYB5gTjRQS5oTm6cGflaZWOMABpDb4In+HPF93Z+cibWoSrObiQWyBLFO2RTEWq1jCmhBbCQawzu7WgFYd2t3KNrUtWAkhvOOTiytlnzuHVrmxpcHuxGOpJ+nmouSpEwsAJzTOBxDy9ymLLDL4pwFJI7ZYERrqpW8XGIy4/GXvI69IBrCiXNJpg6+OXcnNhzTVkkYUXk5psp8jBh/w8XhefaRwy1fY2SFB89ceJRlqXEyd9LAcTaHVTlxuvJbK+ZRZmNWNj63Kl46XOLS1lXzORNLnWjbGEqQbZyAxPN2h6N6FlG0HTfui7lLqiEoT8qIaM+ZeJy4CLMVxBpeI1NZrnVjp6RMhmk00Wp7RQ8A050UrKiytoSIz6Xq5RjG0cuU7qFJgaQdiXkxxh95ESSbTxBimzKLQCh5iZ103Zj1tQ7TjKBVeBx6hrUpXgqUfBz7nAqaO3v0bHkgFLU8GRjfn9RMbGKemZL0mX174XiEowNTMitLkj7PHNCW2Llx80cAgEdWHqllYi1O1FaDOOkYT/SZylfEoFamIPbKhePTdiwfaFomVvuckrusauXEMfx9fyf2tnfDn6bkCYiXDTuVlFIiAFiZ1NjDpcgpjZ0ciox/OXYcnn9vr6mUVlW0bqFdwWjfg1i9yZSsjyTv92cPYgHgv86YhjK3ipc/+FL7G5IuTJOnKpR7VMvuiyLD2dIZtPVDbdeM4WWYNbLc9gWJkJirbD6f+/qYiQWAbzSOSGmCZjyXZfqOiPWExYVTIBRBJGY94JSYE6uVE1t9na2WZyvRs+raAGe5W9XfD49DQVsghBi0LJ9D0dZctQruxPdm+rBSvelKPohy53TnJ7cqQ5G0ufWJTKx5/8S6sla/FR6HYvlbWu5WLYN1cX3hcyp6MHHEkBK88uE+tHeH9fWAjRUvekM3w+cmMrEOi2unXDqPJ6ZT2Zu2YvzszQ3NjMek+fWnDy/D9PjfZTkQavWb71JSMuX5YAyGxXxu7TNMXE/2ZvqO2L/9nUHTIKXx9+CiuaNwxNDSjFVIiedLXBek43EopgHEju6w5SD65Pic0Ld2tAAwd7cWHIqkrzlb7nHg6Qtm6aXRRsPL3Nj+ZQfGVHnzUgllbuxkft+N71O690Ey9LNJl4k1kiUpvtRfJKXBlTB1WCnK3Kpp6Rshec3xCo8DF39lNE6YWAM7smVirz6hHuOqfThyhLmySBwPbYEQojHg8gVj8c25o229ZiaeHAduD6sgVlt/Vaslz1pObDh2jUGkOBEMKXWhyR/Uuou5FJTG541o20gwJrltBbHxwKonFIHqTYy2JI+MSJIUXyIjZjqQrdaMNf7AiC+mrTmxxi5+KRnN1NfJVE5stfSPyKSGozF4HYq+T7mUgqiKBIS1rKW4cBYjzS5VTjnB9GZOrAhMjSPV4ocl3ZxY63Ji82unXSc2TZZXKPc4sKdda0IkJtEnjwaXuFR8tM9vuq3M4mRgfI3kTIM+f1pJZLzFBZdLleEawAIOVZb0OavGUtXb3vy7lkGzuvi0GFxJx/gDkzETq//96c8bvZlj2F8USUJXKE0mVtWWN/nZu7vxP/84gKNHVWQsPbr4K6NRb6PcLDkTG4pG4810FGz51jEp2QSvU8XPt+0xdDDt3ftXV+qKlwZLegOc/Z09pm3SHQtidFmUJCdftOiDYvHv0OXHjtPnEBuJ9+9AVwhD81ipIBrF5Coxl908B7LEZX2Rn4tzGlNH2x02g1hBBAn6UhaW5cTae3qwO5y2YZrV0nF61VH8vmFl7sS8acN0mBqfM9Gd2GKfS10qZEmbA/rK/zVl/Zvs8jkVNCP9++R2KFBk0fMgnolNuhAeVuZCfY1Xb76S/HirC+zk5TKEaHzkbdHEGv1+UcIbicYsu2kbu3YLIhAxNXbqxXc7UeJt7zFWHZyT9yPTMWk1QGQ197U06f3r7ZJgycTfqcgSKj0OBEI98cZO2TOfGZ83fuzs7wymrUIcXenFgvGp3c2teGwEsaLSQbBaxxTQAs9yt4o//EPLxFoHsbLecdrrUDCxtgQTLfpk1ZU60d6tVZ9ly3raYby2Sx4ANg5QZvpcRKM/O5lYAIkgNk05sceh4OV/mqMf3+Piv1vnHjUi5fdBkiRcNn9s1tcUjFU0VtdAIys8+NfjxlnuE5DI9Ptsli9nI74PbOyUhtepoNuwxmk6kpToRGwqJ47/6GnzWzvgdWoBbKlLhU9cOMgyEq0S7JWCqIZMrMj6+JwKIhYjM9ryBOalUex0JwbslecYL/iSgzLza1pnVo1MmVjV/D6GozF4nIoeJOYyiibeoxKXClXRfvB9Tq1rolXm0dSd2GbJibFDYeJ5rANNvTtxmpFx43ZWZdVAauliMmP2Qw9iLTKxYoFxIXl+MKB1Z/xqQy2mDS/DWTOHWe6vQ5b1z8Zu9jrfVFk71oHEki094Si+ONgNj1OxHEW3GsRJRzzeZ1jqyYo43vI1+t7fVEXSLyaS3wexvElnj7YMU0dP+kwsAJx71Mi09xmllhMnus5a/SBdc0I93tvTjkAoghqfM+1xn835R4/E0qlaWbMkSSh3q3omVgz4ZfvOHzu+CnvaRqaUyifPsU9XSi8uVHrCUVtrcfc3URWU/NnnIxNrxZS5snFxb5xDC1hXTSTmxFovRwMYgljD9zJ5zd9rF9Xr53Djubi2xFBObPG9rvA68LPzZ2FctQ8/euNvWf8muxKDhGmC2Hg5sXG/kn9vJEnCsxcebfn44WUuy8BzbJUXVh/52/GSzq8dUaffNrHWp3ejtRqUSe5ODCRKQo2fpfgbc1k+K9M8ZSvmxmXGOcfGwef030mth4nWzd14myAC8FKXYg5u85D1AxJ/p9chOtX2mBo79fZ7Ko6dls6g6XgwDtTZLdkEEu9DpvObNuc88RvQ0RO2PK9LkoR546rw6nZtcCjdnNi2+LItyVOejEpdifVpR1b0fQDRqpxYqDCVE6ffJ5cq6ytE2FlWxudSAX/QMpi32q+pw8rwv1cfm5cGm8bPM5feC4qsJdREdjzXaqF0EplYlhNb8jkVHOiybm+fTJElRJI6nomTW228dMbrVDBndCW8zkTXR638I/E89sqJteftDkX1oPfbC8cjHEkt3bQ6ydvtTpzLEjsAMDZproT5dbTXFxeIElJ/LIzbmwcDEmUzeklYTplYWV9PtcSl4vbTp2L26Aps+Os+lFjUdIrlT8LxdXjtEJ97Lo2drLKexs9JMSyxk1JOrAfI1sfmhJoSbI2PXIruq8knUqtRT6sfKoci4z+XTLF8HUWWtAy5Ilk2txpIqiIB8UazPeGIHiS1d4fR0R3GUIs5qLmtE6u9f9nKqZReDLQUkiJJeoCQXFroUmSEozH4e8KIxLTGDVUZfjztcsWrXIRQNHO3yFOn1OHUKXVp77fL7VAw1PAdK3c70CIufhwKgpFw1kGYoWVuXHn8+NTnFpUXWX5QM134FII3TdbEeKGRy5qn2eSeidW2EY1frM794jdaW7bOel9dFs1mSpKadM00lMEZf/erDZnYdNcDE2tTM519JfYv3e+d29Cszp3lN8HKHcumWQ6c/3Bxg+XyTqdOrsPLf/0Ss0YmlllxOxTUV3vxSXOnZUAsgg5jab11Jrb35cR2s7emObHGhmZpeoZY8ToUdMYi+jWccVDGOCfWVImWr0ysmshWG5djtBooyO15EwNr6TKxdgMF4/NlzMQ6FPOc2O6wXmae7PTpQ/Hq9iYokvXaog5ZxpeBHv150xHfjbauYF4G203n8qTvqHHKUabpb05VxoGkpY0yvmb8nJAuE2slXytEaEt6iiV2cuu9oHV7z28QK457NnZKQ7Ttt5NRESfT5OALSCzC7XMquPqEeqyZP1b/EFXFPJHdzsWCyHh1hxONnSbU+DDZojOf03DSE6wysU7LTKyNINbwAzghaQ0q4+skd651O2SLTrHWFzd6x1KHor8/uZysRZmieL2FE6rhdSqoK3HpnQeT6ftr8+ItsU5s6t+QHMSWulRIgOUPvsdQLiRJhiV2kvbD61QxssKd9sLpX48bh6cvmAUAaA0ETeW+guVanzn8UOnP49Iyk6qeiS1QEGt43Y54GadonrS3vdvyxy1dptuKz2YQa6c7cTFRFVlfYif5eyXeF9H1dW97t2UzsVw5UzKx0YIcNyUuRa9UEIMUvd0Pq+W7rF8z/YVPISQysUnnB2M5cR6rK3LNUIljMlMQa2yClO7iVCwlZAxCk9fDNG+fJhM7gN9r8RlkmhOrr9fq0NayzWWetdepWP7tDkW2nPLw71+diLeuWJAS+E6Jrz1uNVA4ssKDX1x0NGYb1hedNbIcC8ZXmdatbBhSisl1JZa/i+lkWrvXivFvNX6+mebEJvPGl+dKDFynBqslLtWaocDeAAAgAElEQVR0Ls1bd2LDfG1xHnariUysnS771s+b2L9RFYkpQ8bMW2kODeP0jtQZflc98eldYjAgXTkxoB0voys9qIpPBUnmUGR9IDZzJjbe3TeWOh2kN5KX2DFSFVkP/DNdr5a5VH3ql71MrPb35RLE5os2RTEeOObYQNAYxOZr1YrEOrHMxFrSm4DYOEHqi4sby2BlCRISB5vxIlovw1QkU7bPzuiQOGlGY9kvuBz6SGVyhk8rnXPpwZrhZJXDOrGqYd/HVGbKxJqDWKvRbOOPh9VggMcp6xdTOWViZckyYLvhlImIpWmu41K15Wdsdye2CDZFsJM8uji0zI2nLzgK42tSu/yJY018bunmxKqyhBcvmZNxn8QPQmtXyPIYNi3UHQ8qchltFXxOBRKk4gpi4w1ehpW7sfdgT9ruxFZLP6VzKGdiRXlcSmOn+N8gAr3ucDQv81m0da0jaPb3IBCK6o2dBlqJS9XXFhXnpL4GsdlKnU3zqPJwIdVXxjmxRqYldvKYiTXNP7RxcZ9cTpzugrA03gQp3ee3aGItFFnCcMPA5denDkGV12H5e5Ro8CTD51T19yBbVu3MmcPw9+bOLH+VPXomNs3fPHVYqT4Y5HYo+OmKmSkVUfmkdUROfX+/2lCLfR09aZcGTO5WWlPiwl3Lp5lumzq0FE+ePyun/VH17Li9c4dqyCSl68qd7Zj0OhW4o9rAW3vEvHyKXk6cvE5svroTx48DMTVNVLQlKt36lokFgK9OTkwmNf6G5VZObC8TC2irbIiVEtKdOyVJwvUnTUCzP7VZE5C0ikWG76fx+fMxMOdStaaDMcAyuK7wONBuWDLSysgKD7Z80gzAXoayN5nYfHLGq6hyvVb0OBTsadea3nltBOt2jKrwwKlIGFluryv8YRfEigPKViZWEplYYzmxlgEUoyumtQ9FYydZRlRKRFJ2LqCMFxTZ5tCmW0DcHS+dsyqxOmJoKRZNrMERQ7KXRxnPAykNkoyZWHGSla33R9vGUFqUNCcWMJ+ccs3EutTUwzfTqJdVI6pMxOdu/Pwm1ZXgsVWNlu/jBIsOroDhQjqpK3FvLvDFc3UGI3pJu5HxhDmszIWdBwI5jbYmnkdF0JBJK1TTIuN7LzKHw8vc+BPaAViP/hm7OmYjjr/kbqvp9iNfJWT9zfhVSs5CiONPNM0ArJd1ypUr3jDq7t/uwK7WgN7YaaCVuFSI6W16JraXAduUIaU4aVKt3sk0HY9DTvRQKIKBDnFcJw+65GOJHSvG5iDZsl6AVSbWel+WThuK9W9/lrJkkuB1KvjalCGm24aVuXHGTOulHkRAoq/pbrPC4vqTJma8PxeJTKz1a151fL3p31OHZT72+sucMZWYM6ZywF830X/A3jlJZJK6w1HT738u1QEiiOgKRdDeHTYN9utTCgxL7ChS/qbYiN90t0NBmdsBr1MxdabudWMnwzXuMYa1io3np1z6EOhBbKY5sfHzbVcwovfnKM1QTjt7dPrjy2ogwYpx4D5fn0mJSzVVFRlVeh3Y1RrIeJ4fXZkYVLOzXqs4J2SaE9ufnIqMEpeS83We2yHrjTfzVU48fXgZ/vtf59vel8L/2g4wr+GEkU26cmKfU9FLlowZDK8hE2taKy2HJXYAZJ2zqQexKQGm9m/Lxk5uFbeedgQqbZQlib/bai8sM7EZRk7TlROLETOPI9FQJ7c5sZKtMg2rfbHfndg62Jw6tDSn+QjupJI48QPZmzXNjEG/1Q+z8YQpshNWjZ2yqfA4UOJMlHoXLBNreO9Fwypj1sVqhDaXxk7iGMo2/2LQZWKN89KS9ln8O2yYO56PTKxL1ZrmtXQF0doVNDV2GkjGOUj6Wta9HKEvdau4eemUrJl6SZJy6jvQ3xLLvSUFsf3W2Ml+wKBtr722X2/sZP2Yb84ZlYe9SxAX4okmRPHfzAEcnErMiS18xr4Yqfr1jf3PRJyXfWnLiTMfk5ctGIvLFow1NC8yzolNVGOI1SHcDnvL/9ghrg+8DgXnHjUCtyydYtrn3gZm4vHVPqfpN8BYXZLL+rP6e2MjE9sViqAzGEE0llvJspFq+gyyz4kFet9xPVnymsBG4ho60/swyrDig8/GnNihpS7UGNYrH2hOVc55Pixg/lzyFcQCuSVNDr9MrCsx5yCbRCml4YJQ0cqQZg4vwxULx2P2qMSckNGVHjgUCUNKXWjpSmQ57M2JTbxG9kysdfmuuGBz6ye/3n2hxQiU1QLY4odFMuxHpnJi8xI7hsBekfSyGSmS2kwhG5eq5NRJzbgv9rsT5yeASy5p7Esm1qFIetdIq4EYcyY2HsTm+D4B2tpgkWgMz7+3t9f7mg/G74JYdNsUxFp1J44vTWVnHpY2j9qZMvc73X4MliDWOCUg+XtlFWTlc06sv0e7gAlFYwUZ/LBaAzmfpbPp+Fwq2rvDRRHEOhWtu37yZy8u+MT8/HzJubFTfBtR9p3u4s3tUPDCxbNNVQN9IV6nNh7EFmKue7ZM7OFOHLK5dIIXx5PxOFLkxAoT2Y7Jo+LXcYkpYYnt62t8GFXhTgxSqXJev+PimsrjUDCszK3/bufSoNCKJEl46BszMT7pOs7uPP+U/bSTiTWUE3+6Xyu/z5SJzcT4OpmCu/7IxJa6M2Ri48FepvfBOAc5eXksK+fPHoXlM4Zl3a6/OHuRFAKSg9jChJOHXRDrdSS6v2WjZ2INpVLnzx6prVunyDjvaPPSE5PqSvCHKxZAkiT9ghvInlkFkjKxNsuJU4LYpExsb0d6x1V5MX1YKa5ZNCHlPvHD4nYk1h/MVGppKidOyk6LshmnApw0qRazRpWnPD6da0+oz3m5E6slgTIRAwt9vdhIboCVS+OhZJKkdQ7u6AlnzMS6VVlfOLs3S5eItWOLaU6s+FGp8Tn1QN5q9E+VJby8eo7tee+bLp1rYz9yzw4UkjFoS76Aszov5CcTm1gbL9PSKf3NeFHjceT2ne/T64pmSkUw0CFJEjxOJeVCS1xo5Pv96O0SO9mCWEDLaoyqtDc/Khvxmyl6GqhpBoT7U7buxIc7sY58Lp+JVSYW0H67I+Go7fO2VcnsVyfX4auTE13UXaqS10GPRBMv83PmMi0mncaRqddU/RnEikZrv/7bfjz6zueQJW1pp94wJkkyXYNpDT61OdH5Ghgqcamma3gjscxOpoERYy8ZO5lYbQ50YUqJAW2ZompfbzKx2nsgIXPJd3867ILYY8ZWoq07ZOtHXG/sZPhiGNv1W5H0ebTmrGM2xvS53UxschAnTjJ9HcFzOxQ8cm5j2vu010h8McWauh6LL3XacmJF1n8wJEnCzUutl3tJJ9scNSu5NPwBrJfY6Q197nD8ecrdKr4ythLTeznXyeOQ0dFjXQInfsR9LhX1NV6UudU+zbNQ5fy8B319fSOPQ0GV14FmfzBt18JcRgXtZKTEoTtYMrHGgbN05cRG+ZoTG47GTD/+hcg2GT97ERzlUjbXW6ILeLEcI16HkvJZi88530Fsb5fYaU1qwNXfxPlRnxOr95cYuM+shJnYrKq8DtSlWZrFijiekoNY0bDG7ndSz8Rm2N6lSHk9XoyNnYxUWau6ymcXcfG8EnIPYkXTn0xBmah8+WDvQQDALy6a3esBKH2N4Sy/TXJ8KsfB7nDerlO+MqYSXxzstryvSmRiMxwj5R4VpS41vgZ78YdZN32toVdJL32NY2f+yutzVfzvbp7NHVuJuWPtNStQLAJSu4yNLmx1J5aN22cJYmWt/Xpy5zQxEuJzqnAoUq+60mYjTrjJI5GqIluebFRDSU9yY6eBHrlxO3K7gLNaYqc3FFmCSzUsV6PIWHvm9F4/n94IIkMm1udUcMLEGhxXX92npkwFnxOrZ8MT3XbdDhmVHi2IFZUV/W2wlRMbg7bkLopWF8/56k4MJOYuA4U5bkrdifNQopS//z+3YsuwlbjUlAyUqmi/HfkOoHKfE6ttI8qE8zmfKpNKrxP/+fXJmBtvWJTrci754OOc2Kx+dv6snC7+XXogmLx+e/Z5nEaJTGz6z0bLxObveEl0zLauZMv3ALIkadcjVmvaZ3LSpFoMKXWl7VYNJObi727rhkuVMbLCerlDO7Kt4WxU4lRwMI9TOVYlVVkazRhRhvoab8bpSpIkYVSlB39r9g+Ka4bRvRxoEJ/NQJ2/rRx2QWwulD5koXJv7GR/ey0AtDrhJQ6oJ86bZarLz5fkkmVBlSXLMjLthKmgKxQxN8iS5QEt4QIS+2w3K5OvTCygvW/5upBOLBNlEYzE7yuJN6Ho61xA1aIaYSCJ/S91qTgQn2fuVpX4uomdA3byFJ/dYPhBAhKfmyylHu9WSwPkZU6sxfMWpLGTMRM7gHNiRYatGMqJAeB7X51kOZXAZ1hfO1+M58hc1oltC2gDHgP5W2AsDU1X1dSfiu04KUZ2GlAaORXZsqRRBKP2g9jsVWxOVc7rALxLlTFzeBmOiK/Lm3xff5xDyz0Oy9UNMnGqsj53OB1xvt3X0YNhZa4+ZefE70mm5XUEbfC+Z0BWUZgypBTPXnh01u3GVHrwZZps7qHCmDgrFAaxGVh1J7bLWAJia4kdwzZ25sRanUT1uZeKlLVRTW+pimwZsB41qgLTh6WehMV+dYUiph+Sc48egUDIeuJ8f8l5TqyS2/YZX9uh5L0lv1U5sdepQEL+RsZE8FboObHGINbjkFEZn5eSaRH0fBLfycGyxI4xc5x8IWFePkLO2zqxVheKhVpiR+jrOrG9ed1M6wcOpBlpplyUuFQE0zQt6S1JkvRqCVtL7MTPhW025sT2pzK3A05FsnWhnC++IsvYHwqcqmxZ0tj7TGz67U+cVJPXQRdJkvDTlUda3ud1Kv0ywLLu7BlZl5XrDfG+xABU9XG5GHHtYec3PtEZvniqGy6dNwZLpg7JvuEg5jGUExcKg9gM+jIf0FRObKc7cQ5L7MwdU2FZyiAusO00kuoLt0NO+VG4Y9nUtNsnGk0lHmNct2ygJC8JlI34AczHSKjHIeftQjpTJlaWxDrG+QpiEyXQhaAHsYbSeLdD0YNY3wBdfA62cuLkTthGxiBreLkbO1q68jMn1uJiqxBzqY3ZR08/zQG1YuxeWsx8TgWxWCz7hjlyKjJCkYi9NdhlCbIERKIxeBypU2MGytKpQ3DUqPIBHZwSXfULeeF3qHGpsuVvnjj/2W7sJJqzZThvXTR3dC/2sHduPGVSvOoov3pbPpqN8Ziu7uN+i98Or43ziTjn53v+cF+MrPBgZD9UQxYTBrFFzqqxk13mcmIbc2JzaOyUbiH3XDONveVWlZwu5sUPSKEv7nKdE+vMYzmxx6Hk7XNJzIm1PnHU+Jz6Ooh9Vfg5sdrrGrNrxs7LA3XyVAdZEJsp6DYez3oQm6d1YlNfqwDlxIbGI94CZGILfZ7LxudS0d0PVTAORYYsRWy911rmVutmPdDTSozcDgXjq/unaimdSq8T9541vdeN/ShVlddhOVdTnOusBtis2CknHkizRmYu3y02jnilXjgaQ1Uvut0aiWtoW3Ni44PcAzFthBIS5cQMYotSXxo7GX/I811OnI4I0uR+vmDzOOScWsyLC+lCBwCJIN/efqhKbttnMr7Gl79MrAhi0/ww333GtLwEJYChGqFQQWz8MxAjrYosQVVkLJxQjf2dQX2pjP6mlxMXeYAiJC/nZGT8Hh45ohw7WrryEkhYldEWvJxYZGIH4KJUrIdYLOXE6QwrdQH9komVLMvX0xFLMhXyAqhQRGMpyo8rF9ajJ5I6MCPOdXbP23bKiSkzb7zJUl8zsSIgtTNQrS9vxs9tQImY47ALYtva2rB27Vr84Q9/wN69e1FZWYnjjz8eV155JSori+fk3pdMrHGOkJ2gNJdMbDonT6qF26Jrcb4tnFCjL8htR1+X/MkXV46ZanFCzMfo3k2nNvT5OQRPhnJiAHktYUmUExc2EyuWAxB/85gqL64+oX7A9mPG8DKcPm0oJtWVDNhr9kWm+fzG7+FZRw7DhXNG5eU1rbO+A/+dF/PCY0iUog1sJra4swHXLpqASDT/QaxDkXPq2ppuvXOiXJW6VVh15Mh1Tqy4GB/IRl+HGo9DC2L7Oic2l0ysXk7MTOyASnQnPswaOzU1NWHfvn34zne+gwkTJmDfvn34wQ9+gGuuuQaPPPJIIXbJkn4h2MuLEjFHyO6atGLB5t5ecDUMKUHDkP6/yL5i4fictnc7ZC2DVqBsXmI/cutUWulxwOtQMKK8923i+4O4MB+IeVyJxk6FnRMrAoRCNVaq8DhwwymTCvLavZGpnNgY2OYzgCiWxk6yJMHnUuDviQxoY6fRlR4osoShpcV1vkjWXyX4TkVGRLUfHLvi5+HDMRNLA8OpB7H2jrETJ9VCgoShOQzSk5moFOvrlCZHLplYPYjl4MNAEuXEh92c2EmTJuHee+/V/z1mzBhcd911+Od//mf4/X6UlBRHtqMv3YkB8YWyF8SK7XvC0X5vzDTQXKpSFCV2oyrc8DhklNtcH63UreLX/zIPBY69U2RaJzbf1DzOC+7V6xu6EwODp5y30MSgg9W5S5QYex35XWrFGMSKKpRCHTclThX+noieGRyIIHZibQl+96/zi35ObH9xKBKiMft/u3ifBqrDOB1+9DmxNr+TJS4Vp00f2p+7dMgT3+dqb9/mxOZSqcFMbGEUwzqxRfNr6/f74XQ64XYXzwhYX9fIFI2B7Gb+xOv1tpy4WLlVuSgu7OaOqcSvL59n6nSbjSJLfVrrrD/oQeyAZGIL3NhJkaBImReEp1Ti62b1vRM/9D5Xft9L8VqKlOhMWaiR8VK3ClWWLDuj96diOM8VilOVczoniWPD62BrDuofznijoUPtmqqYiUqxvGVic2jsVEzdiQ8H4nzvPdzKiZMdPHgQ99xzD8455xyoavZdUhQJFRXeft8vd/yDqSr3oKLCC0WRc3pdZ/wDrqn0Wa7pmbK9KqMzGEFpiWtA/r6BMrLah52tgUPqb+qLXI+jZNXxOa/V8eOyP5XFSyMryvr/taz4PE64HQoq4ktK+dwqjyODdMdSiVd7v0o8Dsv7HYqEUrf1fb1VB7EckgPlXge+7OhB5QAco1bKvU4427sxc3w1blpyBL5+5PC8NTs7VPX1vFTidiAcjdl+Dm/886gsPbR+76jvx1K++LwO7fejCPblcFEWH8AcN6y8T+uPV5Rq1zlV5e6sn9+CyXU44eNmzKqvQZmnbxlgsm+sLEOWgHFDSgv2Hcvrr/pdd92FBx54IOM2TzzxBObOnav/u6urC2vWrMGQIUPwne98x9brRCIxtLV19Wlf7YjGu911B4Joa+tCRYU3p9cVU2n9Hd0I5NChONgdGpC/b6B88+gRWDFz6CH1N/VFrsdRilAEABAJ9v9x0hMIAUh8BwbaifVVqPM6EA6GAQAOCTyODNIdS6H4+yXHrM+VTkWGS5Hy+l72dAUBaPNjPPGMZKCrMMeNW9Hm4Le3B/D1hhqEA0G0BYIDvh+DSV/PS5d+ZTRisP/9FDkTJc0xSoNXn3/j8qRUlVHldRTFvhwuHJI2VzLUx3NusEe79kA4kvXzcwK4bckURHtCaBOPo37nAvCLi2ZjRIW7X79jtbVWbds0eQ1iL7zwQpx22mkZtxk+PLHGaWdnJy699FIAwAMPPACXK3Wdr0LKx5xYCfbLgx2HaDmxx6GwDDSPxDqYA1HCUeg5sdOGlWHasDL86uNmAIVr7DTYZDt3ORU57/NYRCltqUvVuxUWammmUpda8EZyh5vpw3Nb91T0SShkUxA6tF3ylTFYMWtEoXfjsDJjeDny0fxc/HbYKSemwhlVmb/VMHojr1fBVVVVqKqqsrWt3+/H6tWrEYvF8NOf/hQ+38AuOG5H4kKw992Jc1maRKxleKgFsZRfR4+uxPdPmYRpw9KPTuVLQ60PR40qx/iqwn4/xXeQjZ3sybROLKANSuR7EER0AC1xKfpAS6HmxJ46pQ7jqllCWMxymfNG1Btep8JBkgF2TuNwnNM4PPuGWehL7PDzowwKMknI7/fjkksugd/vx3333YdAIIBAIAAAKC8vh9PZtwnh+dKXdWIB7cI7l07D4kedGQTKRJUlLJ02MB0Ua0pceOCcmQPyWpno6/3xgtcWfYmdNOeuIaUuDM/z0lGigUqJ05CJLVAGf964KswbZ29AlQpDNN1ikEFEyeprvJg5shwTa4ovwUXFoyBB7Icffoj33nsPAHDKKaeY7kueM1tIqtS3cmI1x0ys6Kx2qC2xQ9RX4jvITKw92TKxa8+a3i9r/7pVGSVuVc/EckCO0hEDU1wnloiS1ZS48It/PobzmSmjggSxc+fOxccff1yIl85JIhPbh3LiHC4URcDLcmIiM/Ed5Nxqe5QsQayvn+ZTzxlTicYRZWjp1JprqEWwPjQVJ32dWH6niYioF7jmQAZ9LSd2xDtk2t7+EG3sRNRX4oKXmVh7xDlkoN+v2047AgDwm7/tR7lbRQmzbJSGk42diIioDxjEZqDIEhSp90GlWGjbLjZ2IrImBpLYndgetY+d1fvq+AnVWDjhGMicGkFp6EEsv9NERNQLDGIzqPI6UO3rfZOpOWMqUJHDwssOlhMTWeKc2NxkmxPb3yRJAs9ilIkzvpA6M7FERNQbDGIzOPeokTitD11gT58+LKftRWMnNkMhMitza+t+1pYUR+fyYqdm6U5MVGgsJyYior5gEJuBS5XhUgfuoplL7BBZq/A48MIlszGk1FXoXRkUsjV2Iio0fYkdlhMTEVEvMIgtIvqcWM4jI0oxrCy/65oeyhjEUrE7qaEWiiyhPIcpN0RERAKvcIoIuxMTUT6wnJiK3bAyN849amShd4OIiAYpXuEUEQe7ExNRHrhUrUST8w2JiIjoUMQgtoiwOzER5cPMEWX4z69PxswRZYXeFSIiIqK845zYIqLKzMQSUd/JkoSvTq4r9G4QERER9QtmYouI3p2YjZ2IiIiIiIgsMYgtIiwnJiIiIiIiyoxBbBERjZ1UhUEsERERERGRFQaxRUQsi8F1YomIiIiIiKwxiC0iKpfYISIiIiIiyohBbBGZPqwUs0dXoNzNptFERERERERWGC0VkWnDyrDu7BmF3g0iIiIiIqKixUwsERERERERDRoMYomIiIiIiGjQYBBLREREREREgwaDWCIiIiIiIho0GMQSERERERHRoCHFYrFYoXeCiIiIiIiIyA5mYomIiIiIiGjQYBBLREREREREgwaDWCIiIiIiIho0GMQSERERERHRoMEgloiIiIiIiAYNBrFEREREREQ0aDCIJSIiIiIiokGDQSwRERERERENGgxiiYiIiIiIaNBgEEtERIeF888/H+eff77+7927d6OhoQEvvPBCrx5faIsWLcL1119fkNcOh8NoaGjAunXr8vq8zz//PBoaGvDll1/qt1177bU4+eSTTdsdd9xx+N73vpfX1yYiosGDQSwREeVdS0sLbr/9dnz961/HkUceiZkzZ2Lp0qW4/fbb0dTUVOjdS+vdd9/F2rVrcfDgwULvSp+1tLRg6tSpuOqqq9JuEw6HMW/ePJxxxhkDtl9+vx9r167FH//4x7w95969e7F27Vp89NFHeXtOIiIqXmqhd4CIiA4tH3zwAS699FL4/X4sWbIEq1atgizL+Pjjj/H888/jV7/6FV5//fVC7yZGjBiBv/zlL1DVxE/hn//8Z9x7771Yvnw5ysrKTNuvX79+oHexT6qrqzFv3jz8+te/ht/vR0lJSco2f/jDH9DS0oJLL710wPbL7/fj3nvvhaIomD17tum+M844A6eddhpcLlfG59iyZQtkOTEOv3fvXtx7770YPXo0Jk+e3C/7TURExYNBLBER5c3Bgwdx+eWXQ5IkvPDCC5g4caLp/quvvhoPPfRQgfbOTJKkrMGSkdPp7Me96R+nnXYafve732HLli1YtmxZyv0bN26Eoij4+te/XoC9S6UoChRFybrdYPwsiIgof1hOTEREefPss89i3759+Ld/+7eUABYASktLcc0115hue/PNN3H22Wdj5syZmD17Ni6//HJ8+umnpm3Wrl2LhoYG7Ny5E9///vcxd+5cNDY24tvf/jZaW1tN28ZiMTz88MM44YQTMGPGDHzjG9/An//855R9SZ4Tu3btWtxxxx0AgBNPPBENDQ1oaGjAO++8A8B6Tmx3dzduv/12LFq0CNOmTcOiRYtw1113IRgMmrZbtGgRLrnkEvzlL3/BypUrMWPGDCxcuBCPPfaYabtgMIh77rkHZ5xxBmbPno0ZM2bg7LPPxptvvpnuLc/opJNOgs/nw8aNG1PuCwQCePPNN3HMMcegtrZWv33Pnj24+uqrMXfuXMyYMQPLli3Dhg0bsr7WgQMHcMstt2Dp0qVobGxEY2MjLrzwQmzbtk3fZteuXVi4cCEA4J577tHfYzG/1WpOrBXjnNitW7di1apVAIDrrrtOf85169bh6aefRkNDg2WZ8eOPP46GhoaUY42IiIofM7FERJQ3v/71r+FyufC1r33N1vabNm3Ctddei8mTJ+PKK69ER0cHfvazn2HlypX45S9/iVGjRpm2v/rqq1FXV4dvf/vb2LVrF372s5/B4XDowScA3HfffVi7di3mzZuHSy65BDt37sSaNWtQVlaGYcOGpd2Xk08+GTt27MDmzZvx3e9+F5WVlQCA+vp6y+1jsRj+5V/+Bb///e+xbNkyzJgxA3/+85/xwAMP4O9//zvuu+8+0/a7d+/GmjVrsHz5cixduhSvvvoqbr75ZkyYMAELFiwAoJXaPvvss1i8eDHOPPNM9PT0YOPGjbj88svx8MMP49hjj7X1vgoejwcnn3wyNm7ciP3796Ompka/b8uWLejq6sLSpUv121paWrBixQr4/X6cf/75qK6uxubNm/Gd73wHHR0derBo5bPPPpLasgoAACAASURBVMMbb7yBU045BWPGjEF7ezuee+45XHjhhXjhhRcwYcIE1NTU4IYbbsCPfvQjnHLKKTjxxBMBAGPGjMnp7zKaOHEivvWtb2HdunVYuXIlGhsbAQBTpkzBkCFDcPPNN2PDhg0pZcYbNmzAtGnT0n6+RERUvBjEEhFR3uzYsQPjxo2zVe4ZCoVwyy23YOzYsXj66afh9XoBaNnDM888E/fccw9uv/1202PGjx9vui0Wi+Gpp57CTTfdhNLSUhw4cAAPPPAA5s2bh/Xr1+vzJidOnIjvf//7GYPYyZMnY8qUKdi8eTNOOukkjBw5MuP+//a3v8Xvf/97XHbZZbjyyisBAKtWrUJ1dTUef/xxbN26FfPmzdO337lzJx599FH9trPOOgvHH388nnvuOT2ILS8vx3//93+b3r/zzjsPy5YtwyOPPJJzEAtoJcUvvfQSNm/ejAsuuEC/fePGjXqQKzzwwANoamrCE088gblz5wIAVq5ciRUrVuCOO+7A6aefbjm3FgCOOOKIlLmqZ599Nr72ta/hySefxA9+8AP4fD6cfPLJ+NGPfoTJkyfj9NNPz/nvSVZbW4v58+dj3bp1aGxsTHnORYsW6YMlYt927NiBv/71r+xwTEQ0SLGcmIiI8sbv98Pn89na9sMPP0RzczPOPfdcPYAFtGBo3rx5+N3vfodYLGZ6zLnnnmv695w5cxCJRLB3714AWmlpKBTC+eefbwqmzjjjjJRGTX31m9/8BpIk4eKLLzbd/k//9E8AtCDXaOzYsaag1ul0YubMmdi9e7d+m6IoegAbDAbR1tYGv9+P2bNn48MPP+zVfh5zzDGoq6szlRQfOHAAf/jDH3DiiSeaPq/f/va3mD59uh7Aiv284IIL0NnZiXfffTft6zidTv097+np0cu8p02b1ut9z4fly5dj3759elk4ALz88stQVbVo5gITEVFumIklIqK8KSkpQWdnp61t9+zZA0DLriarr6/HW2+9Bb/fj9LSUv32ESNGmLYTgWl7ezsA6MHsuHHjTNs5HI6smdVc7dmzBzU1NSnBcV1dHcrKyvS/Txg+fHjKc5SXl+Pjjz823fb888/jsccew6effmoK4iVJ6tV+yrKMJUuW4JFHHsGuXbswZswYvPrqqwiHwzjttNNM2+7du1cvxzWaMGECAKT8TUbRaBQPPvggnn/++ZTtxo4d26t9z4cFCxagpqYGGzZswDHHHINYLIZNmzZhwYIFqK6uLth+ERFR7zETS0REeTN+/Hj84x//SGlslKvkDKxgzK5abS/+1yrgS/ec/cHqtdLtu9GmTZtwww03YPTo0bjlllvw8MMP49FHH8WSJUv6tP8iWBXZ2I0bN6K6uhrz58+39Xg7r33//ffj7rvvxty5c3HHHXfgpz/9KR599FHMmTMH0Wi01/veV6qqYunSpXj99dfR3d2NP/3pT9i9e3deSpmJiKgwGMQSEVHeLFq0CD09PXjttdeybiuyqjt27Ei5b8eOHSgvL087/zLX5wyFQhmziL0xYsQI7N+/HwcPHjTd3tzcjI6OjpSssR2bN2/GqFGjsG7dOixbtgzHHXecqQS5t6ZMmYJJkyZh48aN+Pzzz7Ft2zYsXrzYtEYuoGWL030eQGom3OjVV1/FvHnzcPPNN2PJkiU49thjMW/ePHR3d5u2621GOZNsz7ls2TJ0dnbizTffxIYNG1BSUoJFixblfT+IiGhgMIglIqK8WbFiBYYMGYJbb73VcukSv9+PO++8EwAwdepU1NbW4plnnjEFOh999BG2bt2K4447LueAZ968eXA4HHjyySdN2b8XXnghJdi0Iubm2tn2hBNOQCwWS1kmZ/369QCA448/3v6Ox4k1Uo2Zz88//xxbtmzJ+bmSLV26FDt37sQtt9wCACmlxIC2zx988IFp7mswGMQTTzwBr9eLo48+Ou3zy7KckrF999138cEHH5hu83g8AOy9x3Zl+9xE065f/vKXeO2113DqqafC7Xbn7fWJiGhgcU4sERHlTVlZGe677z5ceumlWL58OZYsWYLp06dDlmV88skn2LRpEyoqKnD11VfD4XDg+uuvx7XXXouVK1fi9NNP15fYKS0txRVXXJHz61dVVWH16tVYt24dLrnkEpx00knYuXMnXnrppZTleqxMmzYNAHDnnXdiyZIlcDgc+MpXvmI5d/L444/Hsccei/vuuw9ffPEFpk2bhm3btmHjxo048cQTe5VBXbRoEd544w1cdtllWLRoEfbt24enn34aY8eOtVzrNBennXYa7rrrLmzZsgVjx47FjBkzUrZZs2YNXn31VVx66aWmJXb++te/4sYbb8yYGV+0aBHuv/9+XH/99Zg1axb+8Y9/4Pnnn0d9fb2pvLysrAyjR4/Gpk2bMGrUKFRUVGDUqFGW+2PXuHHj4PV68dRTT8HlcsHn86GhoUGfywto2dibb74ZAFhKTEQ0yDGIJSKivJo+fTo2bdqERx55BL/5zW/wyiuvIBaLYcyYMVixYgXOP/98fdslS5bA7XbjgQcewJ133gmn04k5c+bgmmuusRV0Wvn2t78Nj8eDp556CrfeeismT56MBx98EHfddVfWxx555JG48sor8fOf/xzf/e53EY1G8cQTT1gGsZIk4d5778XatWvxyiuvYOPGjairq8OaNWtw+eWX92rfly9fjpaWFjzzzDPYunUrxowZg+9+97v47LPP+hzEDh06FLNnz8Y777xjmYUFgOrqajz77LO444478MwzzyAQCKC+vh633XZb1sDvW9/6Fnp6erBp0yZs3rwZkyZNwj333IMXX3wR77//vmnbW265BTfffDNuueUWBINBnHXWWX0KYt1uN37yk5/g7rvvxg9/+EOEQiFcccUVpiB26dKl+MlPfoK6ujrMnj27169FRESFJ8UGstMFERERUQG0t7dj/vz5uOSSS3DVVVcVeneIiKgPOCeWiIiIDnkvvvgiQqEQli1bVuhdISKiPmI5MRERER2y/ud//geffvop1q1bhxNOOCFlDWEiIhp8WE5MREREh6yVK1figw8+QGNjI2677TYMGzas0LtERER9xCCWiIiIiIiIBg3OiSUiIiIiIqJBY1DOiY1Go4hEBj6BrChSQV6XDi08jihfeCxRvvBYonzhsUT5wOOIAMDhUNLeNyiD2Egkhra2rgF/3YoKb0Felw4tPI4oX3gsUb7wWKJ84bFE+cDjiACgtrY07X0sJyYiIiIiIqJBg0EsERERERERDRoMYomIiIiIiGjQYBBLREQZfdYawF/2Hiz0bhAREREBsBnE/vGPf8SaNWtw7LHHoqGhAS+88ELWx3z88cc477zzMGPGDBx77LG49957kbwk7euvv47Fixdj2rRpWLx4MX71q1/17q8gIqJ+8/D/7ML1G/+v0LtBREREBMBmENvV1YVJkybhe9/7Htxud9bt/X4/Lr74YlRXV+MXv/gFbrjhBqxfvx6PPvqovs22bdtw1VVXYenSpXj55ZexdOlSXHHFFXj//fd7/9cQEVHetQdCaPYHcbA7VOhdISIiIrIXxC5cuBBXX301Tj31VMhy9ods2LABgUAAt956KyZNmoRTTjkFq1evxqOPPqpnYx9//HHMnTsXl112Gerr63HZZZdhzpw5ePzxx/v2FxERUV51BiMAgH+0cLkDIiIiKrx+mRP73nvv4eijjzZlbRcsWICmpibs3r1b32b+/Pmmxy1YsADbtm3rj10iIqJe6gyGATCIJSIiouKg9seT7t+/H0OGDDHdVlNTo983atQo7N+/X7/NuE1zc3PW51cUCRUV3vztsE2KIhfkdenQwuOI8mWgjqVAKAoA+KIzxGP3EMXzEuULjyXKBx5HlE2/BLEAIEmS6d+ijDj59uRtMt0vRCIxtLUNfEagosJbkNelQwuPI8qXgTqWxFzY7XvbeeweonheonzhsUT5wOOIAKC2tjTtff1STlxTU4P9+/ebbmtpaQEAVFdXZ9wmOTtLRESFE4vF0NmjzYndeYAXFERERFR4/RLEHnnkkXj33XfR09Oj37Z161bU1dVh5MiR+jZbt241PW7r1q1obGzsj10iIqJeCISiiAEocSn44mAPukORQu8SERERHeZsBbGdnZ3Yvn07tm/fjmg0ir1792L79u3Yu3cvAOCOO+7AhRdeqG+/dOlSeDweXH/99fjkk0/wxhtv4KGHHsJFF12klwtfcMEFePvtt/Hggw/i008/xYMPPoh33nnH9DxERFRYoqnTkFIXAKA7Pj+WiIiIqFBsBbF//etfsWzZMixbtgzd3d1Yu3Ytli1bhv/6r/8CADQ3N+Pzzz/Xty8tLcUjjzyCpqYmnHnmmfjhD3+Iiy++GBdddJG+zaxZs3DnnXfixRdfxOmnn46XXnoJd911F2bOnJnnP5GIiHIVjcVw7+//gb81dwIAytwOAEA4yiCWiIiICkuKiY5Lg0goFGFjJxq0eBxRvvTnsfTFwW6c9vD/4qRJNdjyyX4cP6Eav/17CzaunoOhZe7sT0CDCs9LlC88ligfeBwRUIDGTkRENLiJZk67WgMAgAqPyMQOunFPIiIiOsQwiCUiohRiLuxn8SC2nEEsERERFQkGsURElKIzqGVie8LaHNhyt7asOINYIiIiKjQGsURElEIEsUJ5vLFThEEsERERFRiDWCIiStHZEzb9u9zDTCwREREVBwaxRESUwpiJ9ThkOFXt5yIc4RI7REREVFgMYomIKEWXIYj1OVWosgSAmVgiIiIqPAaxRESUwh9MlBP7nApUOZ6JZRBLREREBcYgloiIUhjLiX0uFUo8E8vGTkRERFRoDGKJiChFZ08EXocCAChxKiwnJiIioqLBIJaIiFJ0hcIYWeGGBC0TyyCWiIiIigWDWCIiStHZE0GZx4GaEifK3CpUJR7EsjsxERERFZha6B0gIqLi0xmMYITHgdtOOwLVPieCYS14ZSaWiIiICo1BLBERpegMhuFzKZg2rAwAsKc9AICNnYiIiKjwWE5MREQpuoIR+JyJcU4usUNERETFgkEsERGZxGIx+IMR+JyKfhsbOxEREVGxYBBLREQmPeEoItEYg1giIiIqSgxiiYjIpDMYAQB4DeXEiszuxERERFQcGMQSEZFJVzyILXGlZmLZ2ImIiIgKjUEsERGZdAbDAGAuJ1bY2ImIiIiKA4NYIiIyEeXExu7EipaIZRBLREREBccgloiITBJzYhOZWEmSoMoSg1giIiIqOAaxRERkEhBBrEMx3a7IEufEEhERUcExiCUiIpNgvAOxQ5VMtzMTS0RERMWAQSwREZmE4oGqQzb/RKiyxCV2iIiIqODU7JtonnrqKaxfvx7Nzc2YOHEi/v3f/x1HH3205bbXX389XnzxxZTbPR4P3nvvPQDAO++8gwsuuCBlm82bN6O+vt7ubhERUZ6JQNWpJAWxisxMLBERERWcrSB28+bN+PGPf4z/+I//wFFHHYWnn34aq1evxiuvvILhw4enbP+9730P11xzjem2lStXYvbs2SnbvvLKKygvL9f/XVVVlevfQEREeRSMaIGqqrCcmIiIiIqPrXLiRx99FMuXL8c555yD+vp63HjjjaitrcUzzzxjuX1paSlqa2v1/z777DN8/vnnOPvss1O2raqqMm2rKIrFMxIR0UAJiTmxSZlYNnYiIiKiYpA1iA0Gg/jwww8xf/580+3z58/Htm3bbL3I888/j4kTJ2LWrFkp95111llYsGABLrzwQrz99ts2d5uIiPpLOJ6JdTATS0REREUoazlxa2srIpEIampqTLdXV1dj69atWV+go6MDr732Gq666irT7bW1tbjpppswffp0hEIhvPzyy/jmN7+JJ5980rLs2EhRJFRUeLO+dr4pilyQ16VDC48jypf+OpZkhwJVllBV6TPd7nIokHj8HpJ4XqJ84bFE+cDjiLKx3dhJkiRbtyXbsGEDIpEITj/9dNPt48ePx/jx4/V/NzY2Ys+ePVi/fn3WIDYSiaGtrcvmnudPRYW3IK9LhxYeR6l+9u5ufLq/E/9xakOhd2VQ6a9jqaMzCFWWUp5bisUQ6A7x+D0E8bxE+cJjifKBxxEBQG1tadr7spYTV1ZWQlEUNDc3m25vaWlJyc5aee655/DVr34VFRUVWbedOXMmdu3alXU7Ijp0hKMxPPnHz/H7T1sKvSsUF45G4VRTfx7YnZiIiIiKQdYg1ul0YurUqSmlw1u3bkVjY2PGx/7lL3/BRx99hHPOOcfWzmzfvh21tbW2tiWiQ8OfPmvDga4Q2rvDDJCKRDAShSqnVtooEhs7ERERUeHZKie+6KKLcN1112HGjBmYNWsWnnnmGTQ1NWHFihUAgOuuuw4AcNttt5ke9/Of/xxjx47FnDlzUp7zsccew8iRIzFhwgSEQiFs2LABW7Zswdq1a/v6NxHRIPLaR036/28LhFDjcxZwbwgAQpFYSmdiQFtyhwMNREREVGi2gtjFixejtbUV999/P5qamjBp0iQ89NBDGDFiBADgiy++SHmM3+/H5s2b8a1vfcty7mwoFMKtt96Kffv2we12Y8KECXjooYewcOHCPv5JRDSYbNvdDpcqoyccRWtXkEFsP/m8NYCbXvsYdy6binKPI+O2oUgUTiX1vK3KEnrC0f7aRSIiIiJbbDd2WrVqFVatWmV535NPPplyW0lJScYleFavXo3Vq1fbfXkiOkS1doUwrsqLj5r8aO0KFXp3Dll/3t2Gv+w9iL81d+Lo0Zl7FIQiMahWmVhZQiczsURERFRgWefEEhH1l+5QBF2hCMbXaG30GcT2n6aOIADgQFcw67ahSBQOizmxqiwhHGEmloiIiAqLQSwRFUxbQAtax1VpQeyBAIPY/rLP3wMA2N9pI4iNxiy7EyuyhEiMmVgiIiIqLAaxRFQwrfGgdUyVF4oEtNnIEiZrD4SYHbShqUMLYls6sw8UpM/EyghHGMQSERFRYdmeE0tElG8H4uXDVV4Hyj0O/d92BcNRnLTuf3DGjGH47skT+2MXB70fvfEJVFlCUzwT22KrnDgGl+U6sexOTERERIXHIJaICqZND2KdqPI6c54T+96edgDAWztaADCITRaLxfC7v7dAVSR0h7RsdYudcuJIFKWu1J8HVWYQS0RERIXHIJaICkY0Gar0OlDhdejlxXa9vbMVANBQV5L3fTsU7O8Mpryn9oLYGBwWS+wosoQIg1giIiIqMM6JJaKCaQuE4FAk+JwKqjwOtOY4J/btXVoQy2ZD1j5p6jT92+tQbGdiVdl6iR1mYomIiKjQGMQSUcEc6Aqh0uOAJEmo9OY2J3Z/ZxB/a9aCNH9PpL92cVD7pNlv+vfkISVoC4SyZlO17sRpltiJsokWERERFRaDWCIqmLZACJVeJwCtpLgzGEEwbC9I2tveDQBwqzI6esL9to+D2SdNnRhe7kZdifYeTxlSimgMWcu2te7EVplYdicmIiKiwmMQS0QFIzKxgNbcCbDXPRdIrDE7qtKDTgaxlv7W7MekWh8mxecMTx6i/e+BLCXFoUgMqsWcWHYnJiIiomLAxk5EVDBtXUGMqfQAAEZWuAEAn7UGMKzMbeOxofjjPNjT1t1/OzlIRaIxfNYawImTajAq/h4PK3MByD5QEIpE4VRSxzjZ2ImIiIiKATOxRFQwB7pCqPRqmdixVV4AwK4DAVuP1TOxFW50hSLMECYJhCKIASh1O7Bk6lDctXwaqn3xbHfWTGzUsjuxKkuIAQxkiYiIqKAYxBJRQQRCEXSHo6iIlxPX+JzwOhTsOtBl6/FtgRBcqoyaEi27yJJis+6Q1uzK40ic5r1OBQAQCKWfdxyLxeLlxNbdiQFwwICIiIgKikEsERWEyAZWx+fCSpKEMVUe7Gq1H8SWu1WUurTAjM2dzESg6lYV/TZRIpypeVYkBsQAONNkYgGwQzEREREVFINYIiqIfR09AIAhpS79trFV3pzKiSs8DpQ4tan9nVxmxyRgkYl1iCA2kj4IDcXvs+xOHH88OxQTERFRITGIJaKCaPKnBrFjqjz4sqNHD8AyaQuEUeFxoNStBbHMxJqJ99DtSGRixTzXkI0g1qo7sSJpt0Vi9oPY3W0BBMNRPP2n3TjnsXcRzeGxRERERFbYnZiICmLfQS2IrUvKxAJah+KG+LIw6bR3hzCsrETPxPoZxJp0x0uGPYYgVpYkqLKEYIZMaih+n1V3YhHY2s3ExmIxLF//RwwtdaE7HEVbIIS97d0YWeGx/XcQERERJWMmlogKoskfRKlL1ZsNAcCoeHCzpy17SXFrl1ZO7IvPifUHGcQaWTV2ArTg1E4mNl13YsB+Y6eD3dpn8mVHj95N+qN9fluPJSIiIkqHQSwRFcS+jh7UlTpNt4nldkTAk044EkVHT7yc2CXKiTkn1khv7GTIxAKAU5UzNnYSmVhHHroTt3YlPsfJdSVQZAkfNTGIJSIior5hEEtEBdHU0WOaDwsA5W4tiG3vzpxVFfeXexzwuVhObCXR2CkpiFUkPVC1EoqKTGymINZed+IDAa0D9Y+XTMF9Z0/H+GovPmYQS0RERH3EIJaICmJfRw/qSsxBrFOV4XHIGTOxv/q4GXf/9w4AQIVHhSpL8DoU7G3vxvt72vt1n4vZc9v2YM1z7+OhrTsBWHcnBrTgNGN34nA8EyunLyeO5JiJHVflRZnbgcl1Jfh4nx8xNnciIiKiPmAQS0QDriccRWsgZGrqJJS7HRkzsf++aTte294EAKjwaJnbEpeCTR/uwz89+77tAOtQ0hWM4M7f7sCfPm/H6x81AwC6Q6mNnQAbc2IzZGKV+LI7dsuJWzq1IFaUiTfUlaA1EEKzP2jr8URERERWGMQS0YBrtlheRyj3ONCeIRM7vCzxGBHEGstjD3Znnk97KNq2px2RaAzjqrx69jMQikCRpZRg1KHY605s2dgpx+7ErV1BSEh8TmOq4o272rttPZ6IiIjICoNYIhpw+zriQWxJahBb4VEzBrGyocy1Kp7hazVs39J1+AWx737WBociYeGEanT0hBGKRBEIRVJKiYHsjZ2CETtzYm0GsQGtg7QSf5w3vhySnXWAiYiIiNJhEEtEA25/vJy0Nqk7MZC9nLg9EMYpk2vx/xZPRk08CP5G43D9/tauw69U9d3P2jBjeBmGxrPUbYEQukPRlFJiIPuc2HCmTGw8GH1/T7ut97mlM6iXEgOAN74/DGKJiIioL2wHsU899RQWLVqE6dOn44wzzsC7776bdtt33nkHDQ0NKf99+umnpu1ef/11LF68GNOmTcPixYvxq1/9qvd/CRENGiJzWulxpNyXqZw4HI2hoyeMMZVenDqlTr/92kUT8Nw3j9ae+zDLxLYHQvi4yY+jR1Wg0qsNChzoCiEQisCtWmRiFcnmOrHpM7H3vbUT9/1+Z9Z9a+0K6dlyAPA4tefsCjKIJSIiot6zFcRu3rwZP/7xj7FmzRq89NJLaGxsxOrVq7F3796Mj3vllVfw1ltv6f+NHTtWv2/btm246qqrsHTpUrz88stYunQprrjiCrz//vt9+oOIqPi1BkKQAJS5LYJYt4qD3WHLBk1ivmu5RfArMn4HDrMg9k+72xEDMHt0hT4o0CaC2LSZWPN7G43F8N2N2/HWjhZDOXFqJlYxlHK/vas1a5fh1kBID6wBYybW3hI9RERERFZsBbGPPvooli9fjnPOOQf19fW48cYbUVtbi2eeeSbj46qqqlBbW6v/pyiJC6rHH38cc+fOxWWXXYb6+npcdtllmDNnDh5//PG+/UVEVPTaAyGUG+ZKGpV7HIgB6LBY91UsvVPhUVPuK3OrUCTgwGFWTvzuZ23wOGRMHVqaCOQDQQTC1uXEToty4gOdQWz5pBk3vPIRdrR0AQAccurPg1gSadqwUuzr6MGu1kDGfTvQFTRnYllOTERERHmQNYgNBoP48MMPMX/+fNPt8+fPx7Zt2zI+9qyzzsKCBQtw4YUX4u233zbd995776U854IFC7I+JxENfq1dIctAFADK47dbrRXbHtAC23KLDK4sSajwOg+7TOwfP2tF48hyqIqsZ2Jbu0LoztDYKbmcuLlTC/w7gxGsf/szANaZ2LpSF96+6lj8v8WTAQD/u6s17X71hKPw90RQZcjEulQZEoAuBrFERETUB9ZXkQatra2IRCKoqakx3V5dXY2tW7daPqa2thY33XQTpk+fjlAohJdffhnf/OY38eSTT2L27NkAgP3796c8Z01NDZqbm7PutKJIqKjwZt0u3xRFLsjr0qGFxxHgD0VQU+q2fB9G1JQAAKKqknJ/aG8HAGBkXanlY2tLXPCHoofN+7u/M4idBwJYMWc0Kiq8KCuLQZEldEeBYCSGYV5nyntR4nEgHIXp9sA+PwBgZIUHu9u07GpNlQ8VFt2jAaC6yocRFW582NSZ9r3+ol17nhE1PtM2XpeCqFSYczilx/MS5QuPJcoHHkeUTdYgVpCk1FF5q9sAYPz48Rg/frz+78bGRuzZswfr16/Xg1grsVgs7XMaRSIxtLV12djr/Kqo8BbkdenQwuMIaO7owZhKj+X7oMazhLub/RhfZg6i9u7Xgi0lHLZ8bLlbQVN74LB5f//383YAwBE1iWOqwuPA3tYu+HvCUICU9yIWiaInFDHdvnOfNjgwc0SZHsR2+bvRFk6fMa0rceHLtvTvtXhON8zna4+qoNXfc9h8RoMFz0uULzyWKB94HBEA1NaWpr0vazlxZWUlFEVJyZC2tLSkZFIzmTlzJnbt2qX/u6amBvv37+/TcxLR4NQeCJmWXjES5cRWHYoTc2KtH1vpdR5W68R+0uSHIgHjq336bZUeB9oylRNbzInd79fW7Z01oty0XSblbhXt3enfa9ElOvmz8joVdLOcmIiIiPogaxDrdDoxderUlNLhrVu3orGx0fYLbd++HbW1J5bEDgAAIABJREFUtfq/jzzyyD4/JxENPtFYDO2BUNpAVMx3tVortr07DJcqW3bdBYAqr+OwWif2k31+jKzwwGVYSqfS68CBrszrxCbPid3fqTVgGl+TKN1SswWxHoc+R9lKe5pO0h6HwiV2iIiIqE9slRNfdNFFuO666zBjxgzMmjULzzzzDJqamrBixQoAwHXXXQcAuO222wAAjz32GEaOHIkJEyYgFAphw4YN2LJlC9auXas/5wUXXIDzzjsPDz74IE466SRs2bIF77zzDp5++ul8/41EVEQ6usOIxNJnU31OBYosWTZ2aguEUO5Of9qq9DgQCEURCEUsA7hDzd+bOjC+xme6rdLjwP8d7Ei7xI5TkRCMxEzTN5r9QVT7nBhd6dG3s+jrZFLudqC9O5R2GogIcJM/Z49DZndiIiIi6hNbQezixYvR2tqK+++/H01NTZg0aRIeeughjBgxAgDwxRdfmLYPhUK49dZbsW/fPrjdbkyYMAEPPfQQFi5cqG8za9Ys3Hnnnbj77ruxdu1ajBo1CnfddRdmzpyZxz+PiIpNazw4TVdOLEkSSl0q/BZL7IiledIRnXBbu0LwlB/aQWxPOIpdB7pw4kTzFIxKrwP7OnoQA+BRrbsTA0AoEoNT1YLPls4gakucpnV7s/UnqPCoCEVi6ApF4HOm/pS0xdcCLnWZ7/M4FMssOxEREZFdths7rVq1CqtWrbK878knnzT9e/Xq1Vi9enXW5zz11FNx6qmn2t2Fw9KWj5tx31v/wE9XHIlqnzP7A4iKXHuWea2Alq3rDkdTbm8LhDM+TgTGrV1BDC9393FPi9vOA12IxpCSia3yOhGKxAAgbTkxAAQjUT2gbfYHMam2JKfX18u+A2HLILY9ENLW7k1aC9jrVPDlwZ6cXouIiIjIKOucWCqsV/5vH3a3deNHb3yCWCxW6N0h6jPR8KcyQzDqVq2b/7R3hyzXiBW8Ti1oOxzWIf10fycA4P+z997xbdX3/v9TW7JlWd7bjp3EibM3gQBhQ4G2lNEvq4O2dFLa3lLuLbftr+1t4d7e3i5auqEtq9ACYZcdQgjZ24n3npJlS7JkbZ3fH0fnRLIkW86GnOfjweNBbI0j6ej48/q83u/Xu64gcQTBwrIjSX6pRKw+Vics9cVGogKjE0EKzOImWbY+MwdbDuBKE+7k9IVTuuYmnWbGn8+Q248nECYcFeiPje5RUFBQUFBQOHPJ2IlVOD70jPlQq8R5jJkguVabO0bpdfoTetYUFN6PTJcwDGBM0zfpnCLVWLyfKMD8oWQX94NGp2MCrVqVdE1YWXkkYdiYIp34iBMrboqNTQSJClAUq/R4+rOrGfFMH44lfX6pUqRBFLepPuMs3czTiT/5yB7GfCEumFPAu52jvPj5s8jLUipTFBQUFBQUzlQUJ/Yk4AmEufGvO9k/4OauZxv5/BP7cE8xmkJCEAQ6HBNUxMoi0y0WFRQaB93c+vDulH2kp4r3ukb52tMH+M6LhwnGlQaPZVROrME3SYiGI1Hc/vCUDq6U0htIUYr8QWPQ7acs1yiLUgmtRi0L21RjcqSfSU6s3SsK1qKYE5ufpae+ePrS4vhy4lSkC+Ey6WfmxAqCIJ8zG9schCIC27udGd9fQUFBQUFB4YOHImJPAp2OCdpHJvjNO510Oiawe4L8fGPHlPf5wb+a+cfeQbzBCEvKLQCMn0YCReH04u12B802D42D46f6UGT+sWeALZ1jvNJkp2v0yMBypy+EcYoxOSCK2MlunXOaQCgAY0zE+sMf/HJi23iA0jR9vx9dVAqANkXEsNQHK82K7XSIn01FhtUhEtOVE6cbo2TSqQlFhKQxP+mQBO/qaitfW1+HxahlW/fYjI5VQUFBQUFB4YOFImJPAiMxp2N3nwuAtTV5vNFiT3t7fyjCC43D/PLtdgCWVogi9nRy2RROL5qGPQC02D2n+EiO0DbipTjm7sWPy5muJBhSj2GZLtUYjojYM8GJtXmClFpSi9hPrK7kN9cvZl1tftLv5J7YsFhOfGhoHKNWTW1+VtJtpyJnGifW5U8dwiX16WY6ZsfhFT/3qxeWcOuqSlZXW9nWPaZkBCgoKCgoKJzBKCL2JGCP6y+bW5TN0goLvlCUcBonwjEh3l7qWVtaLva4nSonVhCEhHJQhdMLQRBotonitdXuPcVHI+IJhBl0B1hVbQUSRezYRGqHLh5jinLi0YnpRaxBe2b0xAqCgM0ToCSNiFWpVKypyUs5Jic+nRjg8LCH+SXmpBTh6dCqxVFIqeb5+kMRAuFoymCnrJiInQhmKmLF62FBrAf2rJo8bJ4gnXHuvoKCgoKCgsKZhSJiTwIObwC1SnSJLpxbKM9N9ARSL+LiQ1Xys3RUWsWF6vgpmq346K5+rvnz9hmHsSicHEa8QVngnS4iti12HKtTiFhnmjLTeFKWE8upxukDfaQgow+6E+v0hQhFBEothhnfVx8nYsNRcQOkoSRnmnulJtekTVlOfCS8K7knVkqQznSjQRaxseCpBaXisXaNKinFCgoKCgoKZypKOvFJYMQbpCBbz4M3ibNeX20SS4k9wTDWFK6S5NyuqsqlKs+EQatGp1Exnkb0nmi2d49h9wR5s3WEKxeUnJJjUEhEEAT+sKWbHKNW3hRZXpnL/gE3wfCR+Z+nitbY+JcVlVZUHBmrA6LAqS2YunQ1VTnxaAblxFq1CrXqg98TaxsXrxHpyomnQhcrJw5GonQ6vATCUVkYzpRcoy5lObH0s1TjkKRy4kzDnUYnJBErPpY0AmgiqLRXKCgoKCgonKkoIvYkYPcEKczWywtOc0x0pCsPlnpo77t6gSxycwzaU9ITKwgCh2P9ls8fHFJE7GnCmC/En7b2yP9WAVctKGZPn4vO0QnmZZAue7S82WLnV5s6eeQTK+RzeTJtdi85Bi1lFgMWo1buZ4XMnFijTkMwIhCJCnKZq3MiiEYFlhSJtxIqlQqjVvOBd2JtngBA2mCnqZCDncICh4fE73ZDydGdL7kmbcIGhcRUY5Rm3hMrfu5SabJZL37+mZYjKygoKCgoKHzwUMqJTwIjXlHESuQYxUVcuvJguyeIVq2S0z9BFL6nQsQOjwdw+kKU5xrZ2eti0O0/6ceQCZGowJf+sZ9Xm2yn+lBOCoNuUcTccV4tN62o4BOrq1gS651uHzmxJcVbusbod/l5rTl9OFmzzcOcomxUKhVWk04eD+UPRfCFotOL2JjQihc6oxMhck061Cn6POMxaNVnjog9Kif2yIidQ8PjZOs1VB3l/GnRiU0WsVKJcaqeWJN+pj2xIfKy9PLnLpUjexURq6CgoKCgcMaiiNiTwIgnSKH5iIiVnARPmkXYiDdAYbY+IZQlx6A9JcFOkgt7/dIyAHrGTs8+tF29Tnb2OM+Y+ZFDsc2EtbPy+LcLZ/PV82vlckvXCe6dlvpunz84nPL327rGaBwal5Nx87J0shM7lUMXj+TW+ePEaCapxiD2xX7Q+7dt4wE0Kig0H1tP7OFhDw0l5mk3BtKRn6XHMRFKSgqWqklSzfSV+mRfa7YTiU6fMOyYCMr9sCA6yVq1ShGxCgoKCgoKZzCKiD3BhCNRxnwhirKPLDZzYuWQnjRiY8QTpMicGF5zKsqJn9wzwD/3DqBRIafMpnJdTgdeiTmww+OBU3wkJwfJiS2LC/bJjm2OjKeZ23k8CEcF2kfEUuEDg+6E+a8AUUHgp2+1UWk1cuOKCkAUrM5JIjaVuIlHFrGTnNi8rPShThJnghM77BGF3UwTheHIiJ2JYIRW+9GHOgGU5xoJhKNy+JLE3n435RZDyp7/ilwTn1lbzcuHbTy4rSfp95NxeIPyBo1Etl6jlBMrKCgoKCicwSgi9gQjORIFcaI0Z5qeWLs3mOSwmA2ak+rEBsNR/vfNNrb3OJlTZKYodjwn2uU7GoLhKG+2jgAwNH56ljsfb4bcfrL1GvlcAtCoVWTrNSc0AKx3zEcgHOXmlaJAnex82z1BukZ93LSiAkOsJNhq0sl9k5k7scnlxE5faFrxC2DUahIc3NOVUCR61Jsu9vEAJTkzd2HhSDnx4eFxQhGBhqMMdQKoiCWn97uOfO+igsDuXicrq6xp7/eldbNYXGZhW9fYtM/h8AbJn7R5ka3X4FWCnRQUFBQUFM5YFBF7gpEciqK4crgsvQYVpHVWRzyJPbQg9sSezHRiSXz/v+Xl/NeV88mNucfuE+jyzZRN7Q5++XYHWzpH8QQi1BVkMeQOJJU2ZoI/FOGuDY3s6Jl+UX06MOgOUGoxJM0BtRhPbNl5q10sLz9/dgGF2XoODroTfi+dN/G9mlJPrCAIcllxKocuHqMc/nNEjI5NZCZiDVr1aS9iJ4IRvvKP/Vz34A55dNBMsHuSN7oyRdpc2NcvfnYLSo8+BKwiN1nEttq9uPxhuXojHYvKcmiyeQhPUVIcFQRGJ0IJ5cQAWXqt4sQqKCgoKCicwSgi9gQjjcuJ74lVq1Rkp3FW/aEI44HwKS8ntseCY86pzae2IAudRk2WTpNynMap4oXGYR7Z2cdvNneSZ9Jx9cIS/OHoUbnFD+/o4+12B++0jx7TMW3tGuXpfQN0TyqzPd4Muv2UpQj1MRu0J3SecLPNi1atorYgi0VlOckiNnbexJ+/eVk6IoJYeTA2MbNyYsmJDUWijAfCGfXEGrRqAqd5T+wv3m5nT7+bQDjKu53J59xEMMK3nz9EnzN1D/roRJCCDN6LVEhObPeYj1yjlvKjCIeSKLMYUZEoYnf2iO78qimcWBDnvQbCUTqmCCJz+8OEo0KSiBWd2NP7M1ZQUFBQUFA4cSgi9gRjT+HEQnpRKpcfT769UUsgHD1pvX6S+C6Oc3tyTdrTyomVFvhdoz4umVdEhVVMWB12z6xEc3g8wF939CY85tHgD0W469lD3Pd6G59+bA/9rhMXgjXkDlCaopz0RAeAtdo98qbG4jILvU5/gpMob9rEnb9S6bDTF8blC6FRHekLT4dUTuwPRfCHIvzXKy0A5GdP3xNr1J3+5cRNwx7W1uRRZNbzdrsj6fc7epy83jLCi43J4VnhiLhRk8l7kQppTixAQ0lOkps/EwxaNUVmfYKI3T/gptJqpHiacmdpNu2hofG0t5EqWSYL9ixFxCooKCgoKJzRKCL2BDPkDqBVq5IWnOLInORFmNQzmD9p0SbN4zxZbqwkvuMd5Fyj7rTpiRUEgX6n2BcKcEVDsdwjODTDPsP9A6IjVm4xJCzGZ8qefheBcJRvXjgbgG8/f3jKUsmjxRMIMx4Ip3RiLcYT69i32L3Ux2bQLiwTRcjBoSNurN0bRK0iIYBJErFjE0HGfJmNyYkvJ36zdYSXD9u4ZnEpl9QXTnuM74dgpz6nn0qrkfNnF7C1azTpePcPiO/p9p7ktO3R2KbB0TqxapUKbSwQquEYSoklKqwmBuI2f4bGA1Rapx/ZU2U1kmPQ0jiFiB2dSL2pJwY7nR7XIgUFBQUFBYWTjyJiTzDD436KcwxJi3ZzGsfMHROJFmPiAnW6MKjjzYgngF6jknthxWPSnjbpxGO+EBOhCLefXcOfblzKknKL7EwOzzDcSRpXs7o6j36X/6h6agHe6xzDoFVzzeJS7rm0nsPDHjbsHzyqx5qKIbc0IzTZ6TqR5cQObxCHN0h9UTYgOmlqFRwYPCJCHB4xhEcbl5orCZoOxwROXzjl7NDJxM+J7Xf6UQHfumhO0vci3X1P5xE7bn+I8UCYCquJ9XMK8IWivNshurHbusZ47uAQB2Jl2o2D7qRNCUnYTQ47mgnSmJ1jSSaWKM81Jmz+2MYDlGTQr6tSqVhQaqYpNsYrFQ6vJNgni1ilJ1ZBQUFBQeFMRhGxJ5jh8dRln2Z96p5YWcQaEsstc47BiT0w4ObnG9uJzkCc2WLBMfGlhrmm08eJ7XOKi+aafBNLK3IBsfdSr1HJIi8UycyNG3IHyNZrqC82pxwXkinvdY2yojIXo07DJfWFrKzK5fdbuo+7qByIie5UTmy6cmJ/KDKjzz8VUqhTfZHo3pl0GuYUZtM4GO/EBpJCyaqsRqwmHfsH3Di8wYzCmeJ7YvvdforMevTazC5Xp7sTKwm+ilwja6rzKLMYeGLPAK822fja0wf40SstHBhwM7com4gAe/pcCfd3xJzYoy0nhiMlxQ0lx8GJzTVi9wQJhKOEIuL3pzgns2ObXZhN5+hE2nPTkaa9QiknVlBQUFBQOLNRROwJZiiWIjuZHKMW71Qi1pQoYs0GcVE/UxHbOOjmjn8e4LFd/UkzPadixBNI6uM90U5sq91D2xQhL/FIvauVuUfKFtUqFSU5BobGA2ztGmX9/e/S5Zj+NQ+NByizGKlMMS4kUwbdfrpGfaydlQeILtPtZ9fg9IXY1ZtcEnosyK/dmkLEGrV4g5GEMuZwVOBjf97BZx/fy6D76MulW+3iZzM35sQCLC63cHBwXBYhYmpu4nmjUqlYUm5he/cYh4bG5V7IqTgyJzbKoMtPeW7m4UOne09sv/OIiNWoVdywrJzdfS6+82ITi8stZOk1hKMCt66qRKdRsW8gMTxr1Cs5sUdXTgyg16rJz9Id9ZieeCpyjQiI34ERbxCBxF76qZiVn0UgHE17Xjq8QfQalXz9k8iKzYk92qoJBQUFBQUFhfc3iog9gUSiAnZP6nmOOWlG5kjBSUlOrFEqJ56Z+/D7Ld3y/09VtjcZmycoz4aVyDXpGA+Ej9nRS8ftf9/HTX/dlZHYlkpMJ4ubCquJvf0ufv1OF6GIwO6+6QXkkNtPqcUgjws5NOyhM4349QbD7Op18omHd/PSoSOhO5JbFp/IWhUro5XKP1Px282dfO+lpmmPMR6pFzjVrFXJsY/fIOl0eBnxBjk4OM49Lxye0XPF02zzUJJjSCgHXlSWgzcYkT+zEU8wKVkbYGm5BZsnSDgqZNTXqtOo0KhEJ3ZghiJWcmJPV4EjbZJIr+mji0vJNWpZU2Pl/usWc8vKSjRqFauqrFhNuqQRPHJP7DE4sSadhgWlxxbqJBE/ZscW60cvSbFxl4q6giyAtN+30YkgBdn6pOPM1msQSBzBpKCgoKCgoHDmoIjY40CnY4LeseQk2hFvkIhAynLi7Fg68WRB6PaHydJp0GoSPxpJsEijbzKlbcTL+XMKMGjVNNsyF7GpxEiuUUtUODHhUpGoIJcHfvfF6UVdn8tHcY4hqcT0C+fUMOoNyq+1KYPXPBQr+S7PFceF/Oytdm59eFeSO/TcgSEuuH8LX3xyP002D4/u7JN/t6fPhdmgYXbhEZdSGgczmmYOqCAIbDgwxMuHbXKpbib0uXxU5BpTCpBUvdOHh8TH/vDCEg4OjjM8w+AriVa7N8GFBVhUZgHg4MA44UiUMV+Iouzk831JuXi7MoshIydWpVJh1GnwBMLYPIEZjYGR+mlP15LifpcPq0knh7VZjDo2fG4N91+3GKNOw2fPruaft62iOMdAjkGLO0VPrEmnlt3qo+G7l9Xz9fV1x/Q6JKRU8H6nXz63ZuLEQnoR6/CGUvb+SoFuXiXcSUFBQUFB4YxEEbHHSFQQ+PrTB/jBv5qTficFBpWk6V0UICmcxB0IY0kxfiQ/S09JjoGDg+mTPCfj8oWwe8QgnrlF2Rk7sd5gmIlQJIWI1cUe9/gvHCXBOK/YTJPNQ/Owh6f3Dya5UBK9Y/6U5bSLyiz824VzWFVtZVmFZdrX7A2GcfvDlFqM6DRqSnIMaNQqVCoVv97UKd9OEAQe3tlLbUEW3750Ll84p4YWu1cuV97b72JpeS6auEAjnUaNxahNK2LbHRPy757YPTDlccYjJtumTn81pxCxh4bHydZruGVVJQCbUox0mY5wVKBnzEddQaKIrc4zYTFqOTDoPjIeKoUT21CaQ7Zew+XzizN2/0w6DV1jPqIClM3EiY2Ju6lEbPfoBA/v6D0lbm2/0y+7lxJmg1Z+X9Qqlfz5WoxaxieNtXJ4gwnpz0fDsspcamIC8lgpyNJh0Krpd/mwxUYsZVqmnGvSkZ+lo9MxgSAI/OadzoS0YkfMiZ1Mtj5WcTCDvtieMR9Nw5lfPxUUFBQUFBROXxQRO0O6Ryd4at8A73aMArC/382AO8Ch4fGkRbPkSqSe55m6x9XtC6Wdobmk3CKP3siEjpjAqivMZl6xmWabJ6NS4BFP6jCV3Fif7omYFSsd6+fWVqMC7n7+EPe91srnn9iX5D4LgkDnqJfaNIvwjy8v57c3LGFxmYW2EW/agKdDQ+M8e2AIOPIZffX8Wv73Iwu4eWUFrzbb5fLI7T1OukZ9fHpNFdcuKeMji0pRAa812xmbCNI16mNZhSXpOfKzdIylKSfeERufck5tHi8fHs5oRm0kKjDgSi3gAXkD5MXGYf6xd0B+nQ2lOdQVZFFlNR6ViB0e9xOOClTnJT6vWqWiviib9pEJWcRO7qUGscT3yU+v4vPn1GT8nCadmo5Yj/Rk0TcVkhMb3xfrD0X4++5+eYTVT99s51ebOo/qvTgWwlGB7jFfxq8nx6CV++QlRidCRz1e50SgUqkozzUy4BKd2CydRnZKM6GuIIuu0Qm29zj5y/ZefvJGm7y54PAGKchOfq1ZscefSULx915q4hOP7OHOpw6ckNFXCgoKCgoKCiePM0rE+kMRfMc4euO/32jjv19v4+vPHGTI7eflwzYAQhGBw5PmHUoiNmVPbMzVdE4KShoPhBPG2sSzpNzC8HhAdninoz0mAGYXZDG/2Iw3GJFTfadCWjRPHoUiObHOE5BQLJUTrqq2sqIqlwGXn/nFZgbcfv70Xk/CbYfHA3gCkYTS3VTMLzETigh0jCSXKgqCwHdfauLnGzuAI+NqLptfzHmzC1haLiYe22IC+vmDQ+SZdFxSXwRAcY6BFVW5vHhomPe6xgBYXpmb9Dx5WXo5TXYy27vHqLIauefSerRqNf/7Ztu0o2FsngDhqCCXcE5GKid+Ys8AP3urneHxAK12LwtKzKhUKs6bXcDOHueMx5NI5fJVecnPm5+lx+kLypsfk4OdJIpzDOg0mV9yjDqN7FTPtCcWjjixgiDw36+38n9vtfOzt9o5MOBma/cYGhX87t1uooLA5g4HBwcz3yA6GgRB4L7XWhgeD7CuLj+j++SkmPs7OhE8pvE6J4KK2Jgd23iA4pzkHtapmJWfRYdjgr9u70WFuOmytXuMSFTA6QsljdeBIyJ2JuXEvU4flVYj73WNyRs8mXK69lcrKCgoKCicqWS8onz00Ue56KKLWLx4Mddeey07d+5Me9tXX32Vz3zmM6xdu5bly5dzww038MYbbyTc5umnn2bevHlJ/wUCR9evlwk/fq2VLz25/6gXJKFIlAMDbrm/b0+/izda7JxVI4b5xKeIBsNR9g+4MRs0colnPFUxJ61nUi+tyx+WBe5klsacvkzd2PYRL9l6DSU5BpZX5qIC/pnB4k3qwZscLiW5fCciobjD4aXYrMds0HL1whL0GhXf/9A8Vldb2Tkp3bc9JnjnTCtixd7LJltyCeH+ATc9Yz5Z8JROKvme3M/aNOxhaYUloQf3uqXl9Lv8/OSNNmblm1hYluzEFqRxYqOCwJ4+F6uqrZTkGPjCuhq2dI6x/v53ebXJlvL1vNsxyi/fFkV3ZRpRF5/iGo4KfP9fzYSjgtyHek5tPuGoMOPE5J4xcfOjKoV4zsvSMeYLHZf5pfFIPZ8atSopZGwqZCc2tiHw8mEbLx6yUVuQxcuHbXzruUNYTTq+felc2ka83PPCYe7a0MhvNnfN+BhnshnwQuMwzx0c5jNrq7lyQUlG90nZE+sNkZ/CnTyVVMQ5sZn2w0qcO7sAXyjCjh4nnz6ripIcA7/d3IXDGyQqpB4lZI6VE2f6/nsCYtvANYvLWDsrjwfe6eSrTx2gwzF9GrrLF+JDv9/GcweHZvS6zlQiUYFtXWOK8FdQUFBQOKFkJGJfeukl7r33Xr74xS+yYcMGli9fzu23387AQGpBtH37dtauXcsf/vAHNmzYwPr167njjjuShK/JZGLz5s0J/xkMxz7yIR1n1VhpjJWQPndgiGcPDM7ImW0ccBMIR/n4snIMWjUP7+jD5Q9zw7JyqvNMCeLyG88cZGObg8vnF6d8rOq8LNSq5EATtz91TyzA3MJsjFp15iLWMcHswmxUKhU1+Vlcs6SUJ/f0J/ScpUKaazq5rFkKl5rsHh8t/lCElljwUqdjgtpYUulVC0p45UtnM7swmxWVufSM+RjxBOhz+vjMY3vZ1CaWgNYVTt3TV2k1kq3XcDhFX+xzB4fI0ml48KZlfG19HcWT3ENpfMmoN4g/FKHX6Utyfi+cU0CxWY83GOHO8+vQqpPdp7wsfcqe2EG3H28wwvxicU7njSsq+N+PLKAmP4s/b+1JWgAGw1F+9GoLb7SMAGIvaioscRsgOQYtO3ucNJSYObeuAIBlFbkYtWq2xtzjTOl1+jDp1EkzYMXXqMMTiMiVB8cy+iUeSWBcNq8o5Xubjvie2EhU4MGtPdQXZfPQzcuoLciiOs/E/12zkI8sKuX6pWW80TJCRICeDFKxI1GBn77Zxv2bOvj1O51c9JstGQVljXgC/HxjB8src/nCDEqqLUYtnkCESKz8tXfMh9MXSvk5nEoqrCa8wQgdDu+Mx/asq83nb7es4OaVFXxiVRV3nl/L4WEPD2wWe9JT9cQecWIzu35LPffluUbuuXQu59bls7VrTL6WTMXBwXEc3iA/eaONNntmI8DOZN5ud3DHUwfkChUFBQUFBYUTQWq1NImHHnqIj33sY3z84x8H4Lvf/S7vvPMOjz/+ON/85jeTbv+d73wn4d933HEHGzdu5PXXX2fVqlXyz1UqFUVFRcdy/DPiioYSHtrWy49fa5V/JghwzZKyjO6/q0f8o7yy2sqC0hz29LkwaNWcVZPHknIH77Q7iEQFhsb9bO9x8rkWWU+DAAAgAElEQVS11Xxh3ayUj2XQqqnINdIZt3AWBIFxfyjJAZXQatQsLMvJSMRGBYEWm4crGo6I6C+vq+XtNgeffWwPd66v4+aVlSnvK8+qNSY7sXqNCoc3dY/n/73VTp/Tx88/tmja4wN4ZGcff9rawwu3r6HTMSF/DiqVSnavV8ZG1uzuc7F/wM2BQTcHB90Um/UJgi0VapVK7gWORxAENrY5uHBuAfXFZupjQjIeSbCP+UJ0j4rhQpOdX61GzdfW17F/wM25acpD87N0uP1hQpFoQiltmz3mJheZ5WO9YG4hLn+IH73ayv4BN0srjpQnv3BomBFvkP+6cj5lFkOScyxh0qnRxPTe9y6v5687ernv6gbZcTZo1ayssvJe12ja9y0VvWM+Kq2mlGWiUshQh2OCHIN2RiXDU3HtkjI2d4xyz6VzZ3S/Iz2xEd5ud9A95uPHV80nW6/lyU+vSrjtty6eQ11hNoeGxnmhcRhfKDJl6u+9r7Xw3MHhhJ+1xEYPSQiCwDP7B3nxkI0vnzuLlVVWNhwYwhMI85+XzkU9g1JbqSrDEwij1aj45oZGLEYtVy3MzMk9WUg9vr5QlEvmzfyaPq/EzLwS8btw6bwi2T0HUvb/zlzEihsNZRYDZRYj9314Adsz3IA4NDyOCtBr1Dy8s5cffGh+Rs95ptIYCx/c2DbCObWZlc0rKCgoKCjMlGlXm8FgkMbGRtatW5fw83Xr1rFnz56Mn8jr9WKxJJZb+v1+LrzwQs4//3y+8IUvcOjQoYwf72jQqlXcffEcLp9fxN9uXU6WTkNbrG/UEwinHfMAYtLwG002qqxGCrP1cknx2po8jDoN59bl4/KH2d3n5M2YW3b1oqkXmrUF2QnPGQhHCUaEtE4siPM2W2yeJAe51e6Re2BBFB3eYIQFJUfGmVizdDz+qZUsq8zloW29acNN0s2qValUFJoNcgLpZN5uG2Fzx2hakTuZXb1OIlGBv+7owx+OsrgsefRKfbGZbL2GjW0OXootagWYth9WYn6JmVa7N+G1jsdKC+cWJYtXCWMsnGZ0IiSfI6nKly+bX8xdF81J2wMouZJjk9xYuV95kpt82fxisvUafvNOZ0LZ9hO7+2koMXP5/KIEcTsZaQOgvtjMBXMLeejm5UmC9+xZefQ6/fS7pg+Skuh1+tK6v3kxwd8+4j1uLizAV8+v4++fWolxhqNkJBE7EYzwu81dVOeZuLg+tbBSq1TcsKycdbHFdqpRWRJ2T4DnDg5z04oK/ufDDdx2VhVA0lzjV5rs3Pd6GwcH3Ty8QxzDtLljlIVlOTNOBLbEpU0/f3CYztEJfnx1AxW5qT+LU4UkYi+cW3jMwkWlUnHv1Q189/J6PrWmKuVIpuwZBjsNxmbzlsV9F4qnuJbF0zTsoSbfRH1xNgOuzPIIzgTaRrz81yvNSYGGzbH2jU3toydspriCgoKCgsK0TuzY2BiRSITCwsKEnxcUFLBly5aMnuTRRx9laGiIj370o/LPamtruffee5k/fz5er5e//e1v3HTTTTz77LPMmjVrysfTaFRYrUc3HuJyaxaXL60AYG6JmS6nH1dE4JJfi6/lwPcuTVo0dzu83PCXnfhDUW47pwarNYuz5xbx1+29XLGkDKs1iyuXVfLDV1rY1OWkacjNwnILC2sKpjyW+eUW3usaxZxjRKtRy4FNpfnZaV/f2fXFPLitlx5PkLNqCwiGo+zpHeP2x/fRUJbDE7evBWBTt9jzuGZuUcJjWa1ZfOqcWu58Yi/tLj9rZuUz5A5QkmNAHSvZDCI6HYUFySKvLNeI0x9OOj7buF92O/YOe7huRSXBcDShhzQYjvKrN1v51NmzyDXpOBgra35q3wAatYorllZgMSWLoMsWlPBMrJf3S+fX8dtNHSystGZ0DqyoLeCxXf04glHmxRbDA7EAn9lllikfo8BswBOK0jseQK9Vs2hWftL83umoKhKfM6RRy8+l0ajpcYljciqKEzd2rMD3rl7Ad549yG2P7+XRz55FjlFLh2OCb1w8l7y86cX71UvKWVCWk/a1nTu/hJ++1U6XOzjtOQri5zbg8nPl4rKUj1ldLL7GPpefldV5R/3dPF4U+kVh88S+QTpHJ/jdzSsoyJ/6fVtQnQeAIxhNe/wHYwFhVy4tZ22sPPv5xmEGvaGE+7zcbKcyz8RVi0r54+ZOBnxhGofG+frFc2f83pTGRhoJOi3PHhxmSWWufP0C8Vw61e83wIpcE9+/egFXLCrFehxKna3AJ4vSzxMWBAGjTo0vKmT0+kcDEQxaNXXlufKGU0V+FiOeQNr7R6ICbn+IZpuHtXUFqFWwrXP0tHi/TwSZnkvRqIDNE+A/nj9M9+gE16+u5uzY90EQBFrsXvKydDi8QbrHgyyPfbcyoWd0glabh4vTtOEovD84Xa5LCu9vlPNIYToyKicGUjpNmSRQvvLKK/zkJz/hZz/7GRUVRxZfy5cvZ/ny5Qn/vuaaa3jkkUeSypEnE4kIOJ3T969NxyyriY1tI/z7U/vln7X0OZMcpwfebCMSFdjwpXMoN2lwOidYWpzNdy+vZ32NVT6W8+ryeWZPP4FwlK+cO2vaYyzP1hOKCBzsHmVWfhZ9sX4rbTSa9r51FnGBuKXFjj4qcPvf9+KKlf+22zzy/XZ1OjBo1RTq1UmPtawkG4NWzS9ea8HhDdE5OsF/XjpXLue1u/zkGLQpjyHPqKPF7kn63eZW0X3WqODVg0OMewL89K127rl0rhxis617jN+/04nPH+LCuYX4Q1FMOjW+UJQVlblEAyGcgeT+0bsvnE1DUTZ9Th+fWlmBPxDi0jn5GZ0DNbFe1+1tdkqM4uZES58o8HO1qikfw2rUMuzyMTLupzY/C8/4zF0YvSC6FN3D41TEXEqrNYtDg25q800pn/+i2jx+9/Gl3PnUAT754DbuumgOAJU5+oxe89fPmwWQ9rbFBjU6jYpdHQ7WVaV3dSX2D7gJRwVqcw0pH1MbEUWjIECuQXNcvpvHQsgvums7usZYXW1lRWn2tMdkjdVgN/U7ObsyOaAL4ECsnaAo7jVWW000D7rlfw+5/bzX7uBzZ1dz6ZwCfv9OJ994Yi8Aq8pzZvzeaMKxcKp9/bTZPXz3svqEx7Bas075+y1x1bxCCIVxOo9/enkqynKMdNiSr0Wp6LR7KM0x4IqrPsg3atjf60t7/79t7+U3mzuJClCXZ8TlDzPs9jMy6p1Rj/b7hUzPpTv+uZ9t3U65bWFbq52GfPFv5qDbz9hEiC+fO4u/bOvlG0/u4xfXLmJWhhUIX//7Xg4MuvnBh+ZlHH6mcPpxOl2XFN6/KOeRAkDRFBva09pKeXl5aDQa7HZ7ws8dDkeSOzuZV155hbvvvpv/+Z//4eKLL57ythqNhkWLFtHV1TXdIR03Zhdl4/KH2dXrYkVsPMrk8TUOb5AXGoe4akEJC8stsnDXqlV8ZFFpQv/fVQtLCISjfHRxKTeuqGA6pCAjaUaqOybgpionthh11BZksafPxSM7e/GHo/z7xXP4xKpKXP6wXAp8eNhDfZE55WLLpNNw9qw8dva6UKnEctAtcSEc41OESxWZ9fIolXgODrjRaVRc3lDMm60j/Pi1VgLhKK/EJe1KI0xeOmRjV68LgBuWlQOk7SkF8b3+2JIyvnp+HRq1ii+fW0tdQWblxFV5Jkw6NU1x4U4D7uTSwlSIM15DtNm9zJkmRCodUiiNlNw77g/zwxcO0TM6wZyi9K9hSbmFH3xoHl2jPh6JlaTWT3H7maDTqJlTmE2TLTnwKhV7+8TPKl0Zc15cCfHpMPrFEOf+37KyMqPNtiy9hmKznu4pyok7HV5yjdqEkulZ+eKMU0EQ2LB/kM88vhcBuHJBCTX5WVw+v4gOxwSVVuNRfX5SuNrLh21o1Kqj6jf9oFJhNdKfwWxlEK/rZZMSvYvNBsZ8oaRyWInXmu3kGLRk6zWsqcmjNMdARBBDus5U+pw+tnU7uaKhmD/euIzqPFNCUKB0nV1TbeXX1y9mIhjh7ucOpZ3VHY+UeZBr1PKjV1uS5oMrKCgoKCjEM62I1ev1LFy4MKl0eMuWLQlO6mReeuklvvWtb3HfffdxxRVXTHsggiDQ3Nx8UoOe4oWJFHI0NCnoY1v3GMGIwPUxsTUVZ8/K57Uvnc13LqvPqI9vdmE2ZoOGjTEX0+1LHag0mfPqCniva4wXG4e5emEJ1y8rl8fv9Dr9RKICzcMeGkrS93x+5dxa/u3C2fzt1hWsq8tnd69T7l9y+0PyvNHJFJn1TIQiSbMrDwy6mV9s5hOrqvhQQzHfv2IeH19Wzq5elzzq5ODgOBoVjHiD/GV7D9V5Jq5fVs6yCguXnqDFuUatYmlFLps7R+XE30F3gCydJu08Xom8LB29Th8j3mDK8KdMkEf1eMXNhVebbTy8rYfV1Xlc2TC107CuNp9svYat3WOYDZoZp75OxfwSMfAqkzEYe/pd1OSZUqbEgpiCrIltluQdx57Yo8WoPfLdO2tW5qWM1XkmekZ9RGOCtG+SQOoYmaCuICtBFNfkm3D7w4z5Qjy0vReTTsN/XDKHytgooh9d1cCrX1rLX25ePqPZqRLStaDPKVYDSIFGClBuEWfTTncOC4JAr9Mn9+1KSN+nVGLJNh6gyebhE6ur2PjVdcwpzJZnSQ+5z1xxJW1KfuXcWSwut7CgNIdDcSJ2/4AbrVrF7MJsFpdb+O7l9XQ6JnhsV/+UjxuOCvzxvW5yDFru+3CDWKE0mDpF3xMInzbje/65d4BvPHPwtDme05ERT4CP/Xn7jMe6KShIDLj8uP0h3usa5daHdyeZTQpnLhk1+N12220888wz/OMf/6C9vZ0f/ehH2Gw2brzxRgDuvvtu7r77bvn2L774It/61rf45je/yerVq7Hb7djtdpzOIxexX//617zzzjv09vZy+PBh7rnnHpqbm7npppuO80tMjxQOVJ1n4pzaPFTA8KQFipReWZMm1GYy1hks4g1aNZfPF53LcX+YUZ/kxE79GF9cV8PqaisCcONy0fGtih1f75gPuyfARCiSFBoUz6yCLG5aURFLq83F5Q/TMSI5wlM5seJCLt6NjQoCTcMeFpTmMKcomx9eOZ+rFpZw7ux8AuEou3pdCIK4KLl0fjHFZj25Rh3fuayeMouRP964LG3a7vHgQw3FDLj8cqrzoMtPWa5hWlGRl6WXXZp5Rylis3QasnQa7LGwq929LkpyDPzqukXMKpja3dVq1KyNibC5sVFJx4v5xWbc/jD90wTVRAWBff1ullVOHSYlhTulSpI92Zh04mXt3Lr8GZV9VueJruqObic/fq2V6x/cwevNYgWKIAh0jk5QO6kCQCqTfK3JzoDLz62rKrluaeKGV16WntwUvd6ZEL+ZNNX3+UykwmrEG4zIaerp6HX68QSOjLOSKI6J2FQJxZs7xNE78RUipTniNWryRueZgiAIvHLYzvIKi3y9Xliag80TxDYeQBAE3modYXW1Vd7EPW92AefU5vH47qlF7I9fbWFr1xhfXFfD4jILahXy+LV4PIEw1/xpO3c/d4hxf/i4Bm35QpGMxOjrzXa+9Wwje/pcPL67n80do2zvVgRaOjZ3jNLn9PN/b7XLo8IUFDIlGI7y6Uf3cO9rrTx3YJhmm4e7nzskmyMKZzYZ9cReeeWVjI2N8dvf/habzUZ9fT1/+MMf5B7XwcHBhNv//e9/JxwOc++993LvvffKP1+zZg0PP/wwAG63m+9973vY7XZycnJYsGABjzzyCEuWLDler21a8rP0LCrL4eL6InQaNYVmPUOT+h5t4wEsRu2ME1Iz5aOLS3lq3yA/39jOu52jlFkMFJmnLsnUadT8/GOLGHT5ZSFUkWtChShiJYehPDczYSiNsdnZ62ROUTbj/vCUTiyAzROQn3vIHcAfjlI3Kb13RaUVg1bNls5RavJNOH0hlldY+PeL52DQqo/bKJbpuGBOIUZtKy8dsrG0IpcBt3/aUmKA/DjhcbQiVqVSUWoxMOQWHaPdfS7W1hVkLEjPqc3njZYReRTP8WJ+LLV6Z49Tdg1BDLP59guHuaKhmIvmFtJq9zIeCLN8ikRkEB3YEW9QHrdzKtFq1Dx/+xoKzTNzrheX5/D0/kGeOSBezyxGHW+1jnDJvCIcEyHc/rDcAiBRXyyW7D+wuQsVsH7O9EFZM8Go06DXqAhGhJTp2GcykrPa7/JPuUnQOCRuXi2clH5eEjs/bCmc2Pe6xijPNVIX93lL19XGoXH8oQgfXVx6XDeWTne6x3x0jk7wrYtmyz+TkqMPDo1TkSs6459eU5Vwv0WlFrZ0jiWNGZMYHg/wQuMwN6+s4OOxTdmavCxaUszkfb3ZjssfZmObg3c63kOrVvH87WuO6bojCAJP7hng/nc6uWReEZfNK2LEG+Qji0qTbttq9/DtFw6jQqwsGoltTj6xp39GVR9nEtt7xP7pVruXFw8Np3xfFRTSsandwZgvxKZ2B3qNmuo8E4eHPWzuGM2ovUYQhDPqOn2mkXGw0y233MItt9yS8neSME3371Tcc8893HPPPZk+/QnjoZuPlESX5hiSSsXsniDFM1wMz4T5xWbOn13A843D5Bq1/PLaxRmJO4NWneDkGbRqSnIMYtmcVVzcZepullmMlOca2dXr5MYVFbj9YbkXbzKFsZLSkbgxOtKs29pJ4R0GrZrF5Rb2D7hZVC4udhaVWeQZsCeLLL2G9XMKeLN1hP+4ZA6Dbv+0ogyOlMZWWo3HdMyiiA3QMyaWJp81gxEk59TmY9KpWT6FE3o0zCs201Bi5pebOlhVbZWF7Ma2Ed5qHSEQjnDR3EKe2T+ITqOSHeF0SE7s8RyxcywcjbO/Opai+mbLCLUFWZSYxe8THBmJNFnEFmbrueui2fz3620sr7CckJ7gHKOY9DpVD/WZSHmciE01hkeicXAco1ad5KJLTqxtPLnHv8MxwYISc8LiJ0svtiD8PeYqzsrPmrJC4YPG5g5xtvR5s49s1DSUmDEbNGxud1Bk1qNRiZuG8RSYpVyAUMqWCMlxvTDufvXF2ezrT56H/kLjMLPyTXyooYS2ES+vNdt5pcmeUQZFOnb3ufjpW+3U5mfxYuMwLzYOowIuri8kW5943ZdKnG8/u4Y/vNeNRgUfWVzKhv1DjHgCM944+6ATFQR29ji5dL5YDfWLjR1MBCPYPQHuOK/2AysuJoIRPvf3vVRaTdx5fm3CRrHCzHi+cUgOAA1FItx9cTX/38vN9EyRXyHRYvPw+Sf28cANS6b8G6Hw/uXkWGHvE0pyxJ3krz19gDdbxDJCmycwrTN6LKhUKv7vmoX864tree72s5IWyTOhMs9Er9PHoDSqZwY9lCsrc9nTJ/av+sNRctOUNEvlxLa4krouR2oRC7CoNIfWES9bu8S+zkznux5vVlZZcfpCNNk8eAIRub9tKiRBMrkMcaaUWYwMjQfYHQtIWjODHfvCbD2vfOlsLqmfOkRtpmjUKu77cAOCAH/a2gOIO5Z/3d4LwN4+NyMe0SG5sqEkbT+shCT4p7vd6UxJjoGaPBMCsLgsh6o8Ez1jPgRBYH+/GxUkzF2WuHZJGd++dC5fv2B20u+OB9KsWMWJTUQSsdOVlB4aGqehNCeptDxLryHHoE3qrwpHBQZcfipSLDzjN0eebxw62kOXcwveT72UmzsczC3KTqhi0WnUnD+7gLfbHbzQOMyqamtSS01BVvLGZzwtdlHEzi0+cn7XF5kZGg/IQYUA/S4f+wbcXL2wlM+srebeqxuYX2zmxcbhY3pdUpvJn29axu1nV3PBnAIESAgDlGi2eTAbNNy2tpragizWzsrnkvoiBJgyFO5Mpc3uZcwX4qwaK9+/Yh6hSJT/e6udv+3oo9d5avoa79/UwV0bGk/oDONn9g/SavfyXuco977WesKe5/3C37b3cv2DO/jeS00zKin3BSNs7RrjhmXlVOeZ0KhVnD+7gMJsfVJmRSr+sr0XbzDCGy32aW+r8P5EEbFxlFoM9Lv8bOkc44HNXUQFAZsnKO/Yn0gKsvXHHNpSbTXRO+Zj0BUgP0s3oxLolVVWXP4we/tFoZXOic3Sa8jWaxIWJF2jE1hNupT9wIvKcohEBV5tsrO8IlcOADrZLIqVEj4eCxjJREznZ4uv52hDnSRKcgw4fSG2dzvJM+monaEYMek0J2THuiLXxLKKXNkJabZ5ODzsYVW1lYlQhO//q5lAOMotqyqnfSypnO90CHY6FlZXi6X1i8osVOWZ8AYjjPlC7O5zMrcoO+X3QqVSce2SshO205tj1B73YK8PAtl6LVaTjn5X+sVMKBKl2eZhYZrPpjrPRNdo4giH4XE/4ahAlTXZza/INWI2aLhgTgGvN4/gS9OX9au3O/jhv5o5PDyekH58cNDNtq4xfrWpg1sf2c2rTe+PxVXPmI+9fa6UKfIX1xfh9oexeYIprxWFsU3gVKn2AC02L1VWY4LrKQna1riS4r19othcF3cMVy4socnmyWhBm47Dwx6qrEZyjFo+f84s/vPSeoCUwVJNwx7mx1oIHrxpGfde3SBvbJzJgV/peKN1BBWwpjqPqjwTP/vYQr587iwA9g+4ZvRYx0N0hiJRnt4/yNvtDp49cPSbUOkIRwV29Izx6K4+VlXlcvXCEg4Njb+vNquON0NuP7/b0kU4KvDyYRsvHsp806ljxENUENsW7jy/lq+cOwuzQUuV1Tjtd77f5ZPF63tdYzyxu5+Hd/Se0Z/FBxFFxMYhOZdqlbir+k77KKPeIMUn0Ik9nswuzMLlD7On35VxP6zEiti80I1tYqCJZYry2eIcUewfGHBz22N72NcvzjxNxcIyMTU5HBXk3ttTQV1BNiadmn8dtmHSqTM6lpq8LD65uvKY5xVKru+WzlHmTypRPNXMLhTDjMKRqOwU33l+LQDbup1cPr8oo+qAc2vzuXx+EVknqHf8ZHHBnEI0ahUrq6xUx5y49hEvBwbHWXGKzt+GEjPnzMo/rc6b04VKq1EOpEvFGy0jBCMCyypSz/6tL86m2eZNWNj0xRyiVCWAX7+gjj/8v6XcuKKCiZDoEkymedjDwzv7eL5xmE8+soeP/HEbtvEAUUHg3587xB1PHeCxXf3oNCoe2NxJMM2In5PJa812/ra1G6cveU73U/sGuO7BHahUKi6uT+5BW1uTR7ZeQ0OJmbU1yVUmUguKw5ta5DXbPEkbhQ3F4qaD5JKCuAGQpdMkVPxIo/HiE5JnStPwuJwRAGI4Y6XVmDA6CCAcidJq9zAvdmxmg1YezQUk5Wm8X/EGw2xqd9A0nPyedjom2NadfM6nIhSJsmH/IOvq8mUjYHV1Hp9aU4XZoEn4bKej3+Xjsgfe46UZCKBU7Ohx4glEKMjW86tNHWk3oY4GfyjCvz1zkC//4wBOX4jbz6lhbrEZbzAij/U7E3lwWw+CAA/csISFpTn87t2ujEOZ2mxH2njWzynkE6vFfvsKq4m+aSpwNrePEhXESqlWu5efbWznV5s6+eXbncf2ghROKxQRG4ckNm5aUUmRWc/vt3QhcKSE9nRHmuPZM+aTkzQzReqLfbNFHPeTzokFcYbpnj4XzzcOcXBwnM7RibRJu4XZenlzYNUpFLEatYr5JTkIiKOQ4meJTnWfr55fd8wOWFnss5gIRZg/xdijU8HswmxCEYFep5+9/W7KLQYaSnKozc9Cr1HxlfNqM3qcs2bl8aOrGt73QuusWXm8/uWzqc4zUR1L/H7lsJ1AOCovmE82d100hx9f3XBKnvt055zafPYPuFOWFPtCEe7f1EFDiTmhjzOeecVmxgPhhMRhaYc/lYgtsxiZW2RmcZkFrVqV0q3787YezAYNT31mNd+/Yh6eQJj73+lkV68TmyfI+tkFXNFQzP9+ZCED7sBRL8w9gTBffeoA39zQyO6+o0/HfbXJxj0vHOa/XjzMrQ/vTpjpGopE+fPWHhaXWfjHbatSBtzptWruv24x916d+vtfkKVDRepyYk9ATEif/LjWLB1zi7LZ3nPkdTUOjbOg1JxQzTO7QLxOHRryEBUEWu0e9g+4087+nYxzIsSgO5A0jm5haQ6Ng4kiq2vURzAiJF3DjToN+Vm6D4QT6w9FuOmvu/jmhka+9vTBhHPB7gnwxSf38dV/HuD1ZjuCIPBmq5ihMB6XEB4IR/nio7u5+W+7GJ0IybPgJdQqFYvKLBwYyGzjQRAEfvRqKy5/mJcP2aa/QxoODrp59sAQWToN375kLp5ARK48Ox78fGMHW7vG+LcLZ/PS59eyotLKvFiOQYvNy4g3yBee2MerTTbu39TJ3c8d+sC7gqMTQZ4/OMxHF5dSnmvky+fOwu4J8mZsrOR0tNs9aNQqqiZdi6usJuye4JRiuMnmIT9Lx7VLywCxcufi+kIe3dWnJBt/gDi5CTunOUsrcrmkvpBbVlUgIMiz7U5GOfHxYE5hNtl6Dd5ghPLcmR/zutp8/rF3AJh6Vu2aaivPHhjipUM2DFo1gXBUHjWSiqUVFnxdY6c8mGZRaQ57+lzHPUF2OuL7b4+1v/Z4I5VVt4142dfvkgOcvnFhHb5gJKMU5w8aUohXWa4RjVrFK002VJBRGJjCyeXqhSX8cUs3LzYOc/s5NQm/e+nQMDZPkB9f1YA6zeZKfSz1u3nYI5/rfU4/Bq16yiwEvVZNfbFZTj6WGHL7eat1hM+cVSVvhPQ4fTy4tYcWm4dsvYYfXTUfo06DIAjkmXQcGHRzzZKyjF/zhv2DbOseY2lFLlu7xjBo1ahVYhr8TNnV6+T7/2pmWYWFG1ZV8Z/PNrK7z8VZNXkcGhpnY9sIdk+Q/7ysfspwmsXlqZ1uENPCrSZdShErlQvXp0hfX11t5Z97B/CHIqhUKlrtXnmee/xjS5/DZx/fK28qaNUqFpTm8J3L6uVKErdfbOm4qL5QPijhBNkAACAASURBVB8O28TbN0zqdV9YZuGVJjtDbj+Hhj2U5hg4HHMmUwn5khxDylFN7zc2HBhi0B3g5pUVPLarn7daR1hYloMnEOHHr7YwERQ3Yv/zxcP8eWs2bbHAO7VK3KT+6vm1/H5LN5s7RinNMTC3KDtlKOCScgt/3NKNJxBOG5r4dpuDg4Nu+pw+dvY4qc4zsbPXOeV90nFgwM1nHt8LwOXzizirxopOo2Jbl5OzZyWXyAuCwJ1PHaSh1MyXz61l0O3n0Z19fO7sGqwmHaFIlLdaR+ganeC2s6oRBHGG8pULS7gpLmRsdmE2ahUcHh7niT397O5zsafPhSRdN7Y5uHDu9HkXbXYvf9/dz10XzZ62TSwqCNjGAxRm69HOYAKE2x8ix6A9rhvRzx4YIhwV5OC1VdVWyi0GXj5kS1vhtrvPyb8O28gxaBkYD1JtNSWFnVbGWj36XP60WRFNwx7ml5ipj52Dl80rwqBV80bLyJT3U3h/oYjYOKwmHfd9eAEgJiXKIvZ9Uk6sUatYXG5ha9fYUaWzfnFdjSxi043YgSN9g4FwlDvPr2UiGOGyKaLOv76+jrE1oVPWDytxUX0hu/tcnFd3ckVsodmARgURAeadZk7srPws1CrY2DrC6ERIdvNT/WE/09CqVVTkGukZ83HrqsoZzYBWODmUWYysrray4cAgN62sSFjc7u13U5itZ2maUmKAuUXiIrPF7uGC2GKyz+mjIteYVvhKLCrN4YXGYSJRQb62bWoX2zHiF2i3ramixSaOhPjoolJ5EapSqagvzqbFljxKJh2CIPDQth4G3AHe6RhlfrGZSquRwylCiKZjeDzAXc82Uplr4qcfXUhRQTY/fPEwb7WOsK1rjId39gHi5ug5xzg+ptCsT9kTK/Xj1xcnLyjXVOfx2K5+9g+4Meo0hKOCnG0Qz4KSHJ6M/d363Npq5hWbOTg0zvMHh/j6Mwd56OZlRKMCdzx1gPaRCX5x7SLW1eYzEYzw0NYetGpVkrt6zqw8fg7cv6mTN1rs5GXpMenUzC3KZlaK1pmSHMP7OthJEAT29Lt4eEcvyytz+dr6Oja2jvDDV1pkV9tsEDdgVlZZeWhbD++0j/KNC+poKMlha/cYT+7p5xOP7EGjgu9/eAFXzhUDslJ9j5aWWxAQx+9clEbE/fLtdnqdftQquOO8WpaWW7j9iX281zXGpRmMVonnqX0DZOs1/PiqBhaW5WDUaVhabmF7T+rS6IOD42ztHmNr9xgOb5CdPU4G3AFKLUZuXVXJr9/plNeHC0pzUKlUeIORpABGo05DTV4Wj+3qJxCO8m8Xzua1Jht1Bdns7Xfxu3e7WD+nYNprzZ+3dvN6ywgqFXiDEeYWZXPrqsqUkyz+9F43f3yvB5NOzS+uXZTR5taAy8+Nf93JBXMK+cGH5k0pZEORKI/s7GNdbf6UeSGRqMDT+wZZXW2VTQ61SsUVC0r4y7Ye7J5AUpWjIAj88F8tDI8HCEcF9Fo156aY5iBtqPWN+VKKUX8oQqfDy/lzxHGG91+3GDjSdpDufgrvP5Ry4jQsLrfI40LeL+XEgNz7VZZB+u5kLEYdj39yJTcsK0+ZzCmRl6VnbsxVPW92AV9YN2vK0QKFZgNzj/Oc06NhUZmFv9yyfMpS6ROBVq2iyGwgx6Cl/DRzNg1aNVVWk1zeo7iNiayfXcDl84u4I8OyaoWTz+fOrsHhDfLDV1oSyvP2D7hZUm6ZckFm1GmozjPxXtcYb7WOIAgCvU5fRiMxFpblMBGK0D7ilRM3N7Y5qM3PoiauMsWo0/Czaxbyq+sWcef6xPOovshMu8NLOJJZ+eu+fjcD7gDZeg2BcJRrlpQyt8hMv8uPNxie/gHieLXJhicQ4ScfWUCuSUeWXsuaaitP7Rvk4Z19XLe0jEduXcHvPr7kmN2Zgix9Sie22eYhz6ST+2bjWV4pBgFu63ayL1b2mUrENpSKf1sKs/V8dm01F8wt5I7zavnZxxbh8Ab53ktN/Mfzhxlw+TFq1WyK5T5876Um9g+4+f4V85KcvZr8LC6cW8irzXa0GjUj3iC9Tj+fP7sm5XtRajEy7A6csvLQYw09+sfeQb7wxH7c/jBfWjcLtUrFzSsrEQSBz59dw7cvncuTn17F+jmFmA1avnp+HU/etoqbV1ayvDKXL62bxd9uWcHNKyt49JMruWVNNSqVKq04W1FlpSTHwD9jmw+T6XP66HX6+cI5NTx3+1l8ak0Vi8stWE063m7LrBRVwuUL8XrLCFc0FLOuLh9rbBzcmpo8Wu1evvHMQXb2JJbjv95iR6dRcem8Il46ZMMfjlJuMcjtVm+3OVhdbcWgVbOt28lbLSNk6zWsqU7e7KkvziYQjvLRxaXctKKCB29ezncur+eTa6rocEzQOs0m1og3yFttDswGDRsODPFm6wgPbO7iey81p3zf/rq9l7U1eeQadfzsrQ6igsBzB4e49eHdXPfgDh7e0YtrUu/7A5s78YWivHzYJhsZqQiEo9z51AEe2NzFL97umPK4d/c5GRoPcO2kKpMrG4qJCiQ8T/uIlx+/2sKGA0P0u/x866LZWIxaguFoylY1yYlNt3HUNuIlIiRXvkn36z2GIDiFqdnX7+LaP29nR5oNoslEBYHfvdvF/gE3O3ucPLi1Z0YJ1ooTmwaNWgyxeKXJRu5JFj3HwgVzCnm1yZ4QVDET5hRlc/fFc6a93eXzizFoR6jJU+afZcLcomxUKtVp2TO6qNzCoNvPl9bNOqYRTx9E7lxfd6oPQWEallfm8pXzavnVpk42tY+yfk4BDm+QAZc/qR8vFYvLLDzfOMzdzx3iJx9ZQPeoj3MzqNaQEo8/9egeLEYtN62oYHevUw4fiUelUqWsbqgvNhOKCHSOTiRs9D24tYdAJMp1S8oS2llePmzDqFXzm+sX89iufq5oKGZXryjw2uxeuZIiE95qdTC/2JywSPzIolL29ru566LZxxxoF0+BWU+HI3mx3mL3Mq84ddhdll7D6morrzTZyM/S0VBiTrmhvKhU3Li9dmlZQvnkwtIcvnFBHf/9ehsAP7xyHm+3OdjU7uCiuYW83e7gK+fO4vKG4pTH/OmzqnirdYRPrq5kIhilw+FN24pSmmNgIhRhPBDGkmY83fHGORHCbNTy5/e6eXx3PzevrOAza2uSRklNhz8U4c9bu1lRmcvPP7ZInpLw8eXlfGxJGfoM8iMAqvJMfCPDMWNatYrrlpbxwOYuOhxe6ibNcJbCoy6ZVyRnUmjUKtbPKeC1Jjv+UCTj6QvP7B8kEI4mian1cwr46/ZetnaNoVWrWBWrMIsKAq832zl7Vj73Xt2AIAhEBfjL9h5+9243e/pc9Lv83LyyAo1axaZ2B55AmHPr8lO+V1c0FBOJCtx1YeJ7I2UsNA6PJ1Vo+UMRfrWpkysainm3c5RIVODX1y/htSY7H15UwmO7+nijZYSoIMgbBVFB4H9eb0OrVvO9K+rZ1eviuy818elH93A4lqpdZNbzq02d/PqdTtbV5lNo1rO1a4xBd4BPr6liX7+LJ/cM8PHlqecuv9ZsY2evi2UVFnb0OGmxedBr1NTkm5K+w5vaR9FrVAlp4iBuEF0+v4jHdvWzssrKW60jctnxhljP8ocWlNA95uOxXf3UpWhVsxh1zCs28+Sefq5fVpaQbL6r18lzB8Xk6cm97hajjlyjVg7vC4aj/P/t3Xl4VOX1wPHvzCQz2ZfJQkIWskA2AmRhJyBFKFJxoYiAtcjSVqporRWxWgUUtfpzRetSEZWl7CC7FkEEWQOENYGEQEgg+74nk5n7+2NgypAEAgQkcD7Pw+PDzeUm93py5577nve88xKzKKyq568DQ1vUK+Vm2pCcx6YTBUwfGk5tgxEnnc1Vl9LfLIWVdRzKLue9H9PJr6znpXXHWfD7uCtOx/zpZBFf7s5kaVI2BqOJ2gYT2WW1vPTrTi16XtbMmDFjRiudw01jMinU1jbuotja4gJcGRrhjev5N3d2drY35fteD72DllEx7a97uZ4rifFz5cEuvrdkUnYruqujB3eHeWKjUd9ycdSrgzuj4/zo00TZjri13Wqx9Evp7OvChuQ8ThdXc1+0D3vPlLDpRAF/6tvhio3Zuge6MbCjB5tOFPDTySLqGkz8Y0jYFcvHXexs2HayiA56e+xsNaxPzketMjfiaul6yRq1imUHs+ni62IpzTtVVMW0tSkknZ8/N+L8w3dBZR2z/pvK3WGePBzrx91hXmg1arQ2ahYdOIdRMTchy6uoI6qdE+rLJDMFlXW8v/UUD8X4WsoN7exs8XG0ZVwP/+teVuxSR3Mq2HW6BE8nLZ/+nMGx3ApCPR35bEcGAzt60KuJrsZgrhRZfSSXwqp6Hon3azJJd3OwJdbfhaER3o2mrES2c6Kwqp5YP1d+390fg1FhQ3I+204V4emo5dXfRDab9Hk56Rgc7sWvOnnSN1jPPZHezX7eFVTW8UNqIaeLqimuMRB9vsT0Rll2MJunVxxha1ohm9MK8XTS8dPJIrydtERe5VJfyw7lsCWtkBnDwgl0/1/CoFKprnkKUEvuS8F6B5YezOZIdgVDI72tSmO/2pNpbhDVL8jqOups1Kw+mkuEtxMd9A6NrnGD0cSWtEJqG0x8vvMMybkVLD5wju4Bbo2Wf3J30DK+VyD5lXVsO1nMo9390ahVrDiUw3/P3ztCPR0tI8ru9lqWHczmcHY5ZbUNPDswFJNi7uxd32Bixj0RluWkLhbo7sDgcK9G81OddTYsTcrGUWvDgEtejixJymbunky+P57PvsxSBod5MTbej95B7ugdtBRXG9iSVsi9Ue0sL02WJGWz7GA2z/4qlB6B7oR4OlBVZ6SwysDgcE/eGB7J/V18SQjR46yzZfupYlLyKokPcGNYpDfjegRQWW/kvycKGNHVxyoxvGD2NvPo63sPdmZJ0jlWHc5h6cFsdmeYy8IvJIGKovDW5pNE+7owvLNPo+NE+jizNOkca4/lkVZQxf3RPvyxTwe2pBXymyhvBnXyIljvQG5VPQ838zwb5u3Iov3nqDeaLC8IS6sNjP+POWn3dtLyp76NKyd+PFlItcHIvZ3b8dSKI6w5mkdKXiXb0ov4Ma2QMG/HFt2/G4wmXvtvKmeKa+h2hYqf0moDWhtzRUdVnRHHFiSim1MLeHn9cTJLavjueD5zdmeyIDGLugYTPZu5X7amoqp6bNQtuwcoisITy4+YP4dMCtPvCeeH1AIMJoW+l3muVBSFVzacQKtRoQAOWht+E9WOlYdziA9ws6yy4ujY/Gf4rZnS3yLsbTUtKisT4kquZs3em83eVoP9LfzzCXElNmoVY+L8eH/rKQ6eLeOn9CJsNaoWNVJz1NrQ2deFYZHeLD+UQ89At2a7rV9MrVKxcFw8YP4wLq42YGerbvLhrzmB7vbobNT8kFpAmLcjXo46liVlo9WoeLS7P3P3ZPHTyUIWHTiHwahgVBT+0Me6gZWPsw4nnYb1x/Isx1KpsDRBOnSujM92niHe3xWtRo2LnQ07zy8N1FRTmRuRfHk6alGANzal4edqx94zJaw7lofBqDTZ1OmCAaEeuNnbUlpjuOw8yB5NlHGC+VxePL/uK0BCiB69gy0hno48MyDkiiMvF1emXO66XOjAv/1UMdtPFZNbXtuiUcllB7PZl1nK8M7tOJpbwaCOnk32TcguqyUlr4LIds5kFFfz9uaTxPi5kJJXiYejlgWPxvHMqqPM3ZPF8M4+5gfmyjo2puQzvHM7yzreTVl7NJdoX+dragx2PdwcbHn93gieX5PM6/9NZda95g7sBqOJxMxShoR7Nbrm8QFu6B1sefOHk7z6fSpf/y7WqqnkogPnmL3NvITKhT4UAI/3s/6duVjfID2rDuey4lAOWSU1rDycQ79gPYMvibdgDwd+29WXlYdz8HXREehuT68gd/gJhkV5X3W/C5XK3Hzs0qWcKusa+HpvFt3au1BtMBLgZs/MYeFW+1xoLnYivxJ/N3tqDUY+25FBnyB3Rp7vxqtWqXj2V41jMMrHmSgfZyb364DRpFg9m1wYjT6QVWZVofDx9tP8fKqI00XmRlY+LnY80MWHzJIaegS68dmOM2xMyefhWHPlS3pRNdlltTzWs3FVCph7Gbw3Ipri6np6BLhZpqOtnNgD/flY9XGx49+PxlNa2vQSatG+LgyL8mb1kVz+3C8IO1sNC/afpdZg4pNRXSwvIC7l72bPoXNlHMutIDGzlCn9g/F10fHVniyS8yp4af1x5j8a1+jeUFHbQEZxNe4Otvi72TN722nWHTN3lv/P/rPYqFXMujeS2EtWMdicWsCL61Is9zFnnQ2fPdztso1OG4wm3v0xnUgfZ34Xb/5cG9cjgDPF1cxPzOL+aB8CbkAVZEpeBd+l5HMiv5IDWWVEtHPio5FdLAN5zdmbWUpybgVT+gfzYBcfXO1t2ZCcx67TxdBEDF5w4GwZx/MreWlIJ/qHeqBRqdDZqllzNJfNqYUtWgpTklghhBBt3v3RPizYd5YpK45Q12BiTJxfi0shAR6Kac/aY3mNRmxaQqVStXj09WKa82WVi/af4+dTxYC50+uwqHaM7NaeuXuyeHFdCkaTglGBcT0CGr1YValUdPJ05HBOBf8e3Y13tpxkxaEcxsb5UV7bwIvrUqioa7Ca96fVqJjUO9BqzdUbqZufCz7OOv7YpwP3RbfjeH4lf156GGi62+8Ftho1f+gdyKmi6mtqVngpFztbvv9zn+s+zqVCPB2JD3DloW7t2ZZexNKkbB7vG3TZiqjsslo+2JpOvVGx9CT4ancmLw8N477o/41enS2tYdyCJCrqGnDSmRsFeTlp+WRUV7LLatGoVTjb2fCnPh2YsuIIb2xKZcqAEP62Opnk3Aq+3J3JPZHe/KqjJ/EBrlYjgqeKqkgrqGpU6nqz3NXRkwm9AvlydyaPxPsT5ePM3sxSquqNTZZu26hVDItsx7KD51CpVHy+4wxv3mdOfmsNRhbsO0usn3n0L9bflbSCSgqrDI26T1+sRwc3bNQq3v0xHTsbNb2D3JlxT3iT83mnDe6Ij4sOHxcdKpWKjp6OvDE8kl4dru0FQJSPM1/tyaTGYMTeVkNKXgUvrUuhrMbAMyM607mZEf1QT0c0KnMSe3eYFz+dLKKq3si4HgEtfgllq1Fz6bvrMC8nnHQa9mWVWpLY5NwK5u3NwkGrQaVSMez89otfDm1OLWRjSp4lid1+vsFd/5DmR+Gaqr642t/x+zr7mCsr0ovoH+rBsqRsBod7NftSCyDAzY7vU/JZuO8sDrYaRnbzxUlnw68jvNmVUczTK47y1Z5MJvcLYnt6ES+tT6GDuwPpRVUYjAqOWg0vDunEogPneDimPX5udhzJLie1oIopyw/z9e9iLVNDjuWU8/KG40S0cybAzQ5vJx3fH89nyoojrJrUo9nBg22niimorOeFwZ0YEOrBryPM17ywqp4H55Tw+c4MnhoQwszvTpCcW0GfIHem3xN+XYMlX+w8w793nUGrURGkd+ChmPasPpLDxEUHGdTJk5WHc4j2dSavog4btZqJvQIYdH7d8Hl7s/B01DL2os/c3kHuvL/1FDnltc2ucrHpRAF2NmqGRnpbXYt+wXq2pBXSYDLhamfLjN92bfbnliRWCCFEm+eks2Hu2Bj+vi6FDnoHnrnK+cyhno78OKVvkx0/b6S/Dgzl4dj2HMupILuslqM5FYzvGYC3s44YPxcOnivn6QHBDAn3arbJ4FMDQiitMRDl48xDMe2ZvvEE/9l/jp9OmruOz30kBj9XO2zUaoqr63HUai47OtfaIts5s/ZPvaz+/s4Dnfn+eP4VRxRGxzU9P+9WYm+r4bOHuwHmVQ42puSTmFnCXR3/N9JdWdfAtvQiSmsMPBzTno+2nUKlUvHVI93Ir6gjyseZqauTmZ94luGd26EAP5wo4POdZ1Cp4P/uj+LlDcc5llvBEwlB2GrUVg3EenZwY1LvQObuzmT9+fVUn/1VKEeyy1l/LI8Vh3II93Zi9shoy2jXpuMFqFVw91V2+21Nj3b3Z/nBbF5Ym0z4+TnizTVJApjSP4g/9Alk/r6zzN2dyfj8AMK9nVh2MJviagNvDI+0jOC0ZLTKUWvDXR09yCyp4cPfRl+2kadapWJCr0CrbVfbKfliXXxdMCnw/tZ0xvcM5Llvj6FSqfj4oS5E+zbfVV1noybYw5ET57t7r0/Ow8dZR1zA9TVm1KhVxJ5fuuvCkjvvbElH76hl6fh46htMTTbxHBbpzextp8ksqSHQ3Z7t6UVE+Tjf8KaocQGutHPWsTElH61GTbXByIiujcuXLxbgbo+COYF66HwCe0GfID0DO3qw/GA2d4d58vKG43g76bC3VfPbrr5E+Tjz6vep/GP9cdq76HhqQLA5cYw3r4l77+d7WHcsj78ONCexn+7IwFlnw0cjoy1l332D9fx52WG2nixkWKR134Hc8lo+3n6aI9nl+Djr6HdJKa6no5YxcX58szeL7enFqFTmapXvUvLZfqoYOxs1CSF6nv1V6FXNzT+eV8GXu88wJNyLF4d0slyTweGevLz+OF/vNXctP1lQhbezjup6I9PWpvDlWB3eTlr2ZpYyuV8Hq5fGfYL0vM8pdmWUNJqPDtBgUtiSWkhCiEejZP7uME+2pBWy6rB5brMksUIIIW57Pi52fPVI7DX/+5udwF7g52qPn2vjB+6x8f7YaMyNVi5X+nrxOq13h3mx4lAOH/x0CluNipnDwq1Gohy0t8YUme6BbpbyxdtJNz8XHLUadpwupm+wno3J+ZTXNbAs6RzZ5ea1ZA+dK2dLWiF/6tPBnKycf8Yb0dWHN384yYn8StYezWPpwWzau+h4+/4o4gPcmDa4I/MTzzKiS+OHQpVKxeR+QfQL1nPwXBmB7g7c1dGDsXF+1BqMbEkr5I1NaUxadJBn7golp7yWxUnniAtwa7I79M3ipLPhb4NCWXwgm/1ZZVTUNTA0wqvZKgobjRonjZpH4/1ZlpTNZzsy+PvgTszZlUlCiL5FJYiXMq8lfWNK6S+nT7C7ZU3eVYdzsVGrmPtIzGVHji8Ib+fEjlPF5FXUsedMCeN7BlxxqZ6WeLCrL8+vSeZ38w7wzMAQjuSUM3VQx8smRUMjvPnXzxl8+NMp/j64I0dzKi5bwt1a1OdHhucnZlFrMOKss7niCgt3hXryZEIdtho19zbRvG5UTHu2niziD4sOobVR8/FDXaxGiNMKqliw7yzPDLRes1fvoKV3kDtbUgt55q4QkvMq2XOmlKcHBFtdu7gAV3xddGw4lk9nHxdWH8nFx0VHt/YuPPvtMcprDXg56XisR0CT81GfSAjC1c6GlYdzmHFPON38XLkn0pudp4uprGvg++MFVNWbu863JJ6Lqur5x/rjuDloeWFwR6ukPs7fjcWPdSclr4IegW6W41XXGxn1VSJv/ZBmKbsfGmHdIC9Ib087Zx1bUgsY0cXH6mdpMCmsOZJDSY2BIRGNXwL1C9HTxdeZ30S1u2J/BpXyS/WEvw4Gg7HZOvkbyc3N4Rf5vuL2InEkWovEkmiKSVHMjX6cdZYuylcisdR6nl+TzL7MUlzsbDhXZu6E2t5Fxyv3hLM0KZstaYV4O2lZMbGH1YNwWY2Bez7bjZu9LYVV9YyN8+OZgSGtkpyAeX70KxuOW5LpOH9XXh4a1uq9P641ljKKq/m/zSeZ3C/I6sVMc77ak8knP2cQ4GZHfmU9ix+Lb5N9TI7llLM5tZBIH+cWj+xuSS1g2toUege5szujhGUTulvND74eR3PKeWLZYeoaTNjbalj/eK8rzvVffOAc7/6YTkdPR04WVvGfcXHXvbRiS+LIXGK7l7oGE0MjvCxzq6+Voig8/PU+skpq+Pihro1etDWYFNIKKpt80bDuWC4zv0vl01Fdmb3tFOfKalnzx56Nrt2nP59m7p4sAFSAcv6/Ho5aPvht9GWnWFzJwn1n+eCnUzzQxQcPRy2nCqvwd7NHrVLRtb0LfYPdsdWoKais4z/7z7HpRAFlNQY+HNmyNYUv2JxawAtrU9Cozb0nvv5d45fH8xOzmL3tNC8O6WRpUJhbXssLa1M4lluBv5sdi8bFX7EM2sur+c8wGYkVQgghbiNqlarJpk3i5hjeuR1Hc8pp72rH1EEd6ezjjJNOg41GTQd3e3Ir6pjUO7DRw5urvS33Rbdjf1YZT8YGMa6VRtcu6ObnyuLx3dl7poT2rnZ0bKb5zS8lSO/Av0Y1Xzp4qdGxfiw/mI3RpPDm8Mg2mcCCubt658uUDzclIcQDZ50NuzNKiPZ1brUEFsxNk/46MJQ3NqXxm6h2LWpWNzq2PUVV9Sw7mE2w3oGOns03LmpNno5aRsf6MS8xy6p8/1qpVCrevC+K0mpDk5UiNmpVsyPld4V6orM5yZTlhzEp8O6DnZu8diO6+nLgbBk9A90Z0dWHzamFHMkp568DQ6+pt8LFxsb7UVRVz4J9Z1Ewr42743QxJgXmJSpEtnPiro4ezNt7lnqjifgAV/7YJ+KqlmcDc8XPpN5VfLk7s9llyn7X3Z89Z0p4Z8tJbDUqNibnsy+rFHtbDTOHhTMk3Ou6q59kJPYqyJtq0RokjkRrkVgSrUViSbSWmxlL1fVGtBpVoyVs7gRvbEpl1eFcnr+7Y4vWxL4aiqKwLb2Y7oGuV9VxvcFowqRwVU31mtPSOKqqb2D1kVxGxbT/xaaEXHAku5xVh3Po5ufCA02U/d8sp4uqUamwvNxoMJr4IbWQNzalUmMwMSDUg78ODLmuFz+KopCcW0FEO+dml+IprTHwp8WHOF1cjaudDSNj2nNf53ZX9X0vNxIrSexVkA950RokjkRrkVgSrUViSbQWiaWb43RRNbO3neLVYRE4291+hZUSR60vo7iakmpDo6WAbqTc8lqWJGUz2Q3fnQAADHJJREFUOrb9NXWZlyS2lcgvlGgNEkeitUgsidYisSRai8SSaA0SRwIun8TeefUXQgghhBBCCCHaLElihRBCCCGEEEK0GZLECiGEEEIIIYRoMySJFUIIIYQQQgjRZkgSK4QQQgghhBCizWiT3YmFEEIIIYQQQtyZZCRWCCGEEEIIIUSbIUmsEEIIIYQQQog2Q5JYIYQQQgghhBBthiSxQgghhBBCCCHaDElihRBCCCGEEEK0GZLECiGEEEIIIYRoMySJFUIIIYQQQgjRZtxRSWxiYiKTJ0+mf//+hIeHs3LlSquvFxYW8sILL5CQkEC3bt2YNGkSGRkZjY5z+PBhJkyYQGxsLLGxsYwZM4bi4mLL18vKypg6dSrx8fHEx8czdepUysvLb/TpiZvkeuPo7NmzhIeHN/lnzpw5lv3q6+t57bXX6NWrFzExMUyePJnc3NybdZriJmiNe1JBQQFTp06lX79+xMTEcP/997NmzRqrfeSedPtrjVjKzMzkySefpHfv3sTFxfGXv/yFwsJCq30klm5vn3/+OSNHjiQuLo7evXszefJkUlNTrfZRFIWPPvqIhIQEunbtyu9//3vS0tKs9mlJnJw4cYJHH32Url270r9/fz7++GMURbnh5yhujtaKpU8//ZQxY8YQExNDeHh4k98rOzubyZMnExMTQ69evZg1axb19fU37NzEreGOSmKrq6sJCwvjpZdews7OzupriqLw5JNPkpGRwSeffMKqVavw8/NjwoQJVFdXW/Y7dOgQEydOpGfPnixZsoSVK1cyceJEbG1tLfv87W9/Izk5mS+++II5c+aQnJzM888/f9POU9xY1xtHvr6+/Pzzz1Z/pk+fjkqlYujQoZZjvf7663z//fe89957LFy4kKqqKh5//HGMRuNNPV9x47TGPWnatGmkp6fzySefsHbtWh588EGef/55EhMTLfvIPen2d72xVF1dzcSJE1EUha+//ppFixZhMBiYPHkyJpPJciyJpdvb3r17eeSRR1i8eDHffPMNGo2GCRMmUFpaatnniy++YO7cubz88sssX74cvV7PhAkTqKystOxzpTiprKxk4sSJeHh4sHz5cv7xj3/w5Zdf8tVXX93U8xU3TmvFUn19Pb/+9a957LHHmvw+RqORxx9/nKqqKhYuXMh7773Hd999x1tvvXXDz1H8wpQ7VExMjLJixQrL30+dOqWEhYUpKSkplm1Go1Hp3bu3snTpUsu20aNHK++9916zxz158qQSFham7Nu3z7ItMTFRCQsLU9LT01v5LMQv7Vrj6FLjx49XJkyYYPl7eXm50rlzZ2X16tWWbdnZ2Up4eLiybdu2Vj4LcSu41liKiYlRli9fbnWsgQMHKnPmzFEURe5Jd6JriaXt27cr4eHhSmlpqWWf8vJyJTw8XNmxY4eiKBJLd6LKykolIiJC2bx5s6IoimIymZR+/fopn3zyiWWfmpoaJSYmRlm0aJGiKC2Lk4ULFyqxsbFKTU2NZZ9//etfSkJCgmIymW7GqYmb7Fpi6WIbN25UwsLCGm3funWrEh4ermRnZ1u2ffvtt0p0dLRSUVFxA85E3CruqJHYy7lQdqDVai3b1Go1Wq2W/fv3A1BUVERSUhJeXl6MHTuWvn378sgjj7Br1y7Lv0lKSsLBwYG4uDjLtvj4eBwcHEhKSrpJZyN+KS2Jo0tlZWWxa9cuHn74Ycu2o0ePYjAYSEhIsGzz9fUlNDRU4ugO0dJYiouLY+PGjZSUlGAymfjhhx8oLi6mb9++gNyTRMtiqb6+HpVKhU6ns+yj0+lQq9WWfSSW7jxVVVWYTCZcXFwA83SYgoIC+vXrZ9nHzs6OHj16WGKgJXFy8OBBunfvblU1kJCQQH5+PmfPnr0ZpyZusmuJpZY4ePAgoaGh+Pr6Wrb179+f+vp6jh492nonIG45ksSeFxISgp+fH++//z6lpaXU19fz73//m9zcXAoKCgBzsgHw0UcfMXLkSObMmUP37t2ZNGkSx48fB8zzjvR6PSqVynJslUqFXq9vNLdI3H5aEkeXWrZsGe7u7tx9992WbYWFhWg0Gtzd3a329fDwkDi6Q7Q0lj788ENUKhW9e/emS5cuTJ06lXfffZfIyEhA7kmiZbEUExODg4MDb7/9NtXV1VRXV/PWW29hNBot+0gs3Xlef/11IiMjiY2NBbDEgqenp9V+F382tSROCgsL8fDwsDrGhWNKLN2eriWWWqKpWHJ3d0ej0Ugs3eYkiT3P1taW2bNnk5mZaWmks2fPHgYMGIBabb5MF+YFjR49moceeoioqCieffZZunbtyqJFiyzHuvjGfYGiKE1uF7eXlsTRxRoaGli5ciUjRoywmlfdHEWaXtwxWhpLH3zwASUlJXz99desWLGCSZMmMW3aNMuLNZB70p2uJbGk1+v58MMP2bZtG3FxcXTv3p3y8nI6d+5sFW8SS3eON998k/379/PRRx+h0Wisvnal/98tiZNL97nw+SaxdPu5nlhqieaOIbF0e7P5pX+AW0l0dDSrV6+moqICg8GAXq9n1KhRREdHA+Dl5QVAaGio1b8LCQkhJycHML9RKioqsrpZK4pCSUlJozdF4vZ0pTi62I8//khBQQGjRo2y2u7p6YnRaKSkpAS9Xm/ZXlxcTI8ePW74OYhbw5ViKTMzk/nz57N69WoiIiIAiIiIYN++fcyfP5/XX39d7kkCaNl9KSEhwVKObmNjg4uLC/369cPf3x+Qz7c7yRtvvMGGDRv45ptvCAgIsGy/8BxUUFBgVb5ZVFRkGVFrSZx4eno2GiUrKioCkFi6zVxPLLWEp6cnBw4csNpWUlKC0WiUWLrNyUhsE5ydndHr9WRkZHD06FFLmae/vz/e3t6cPn3aav+MjAzat28PQGxsLNXV1Vb1/ElJSVRXV1tKKMSdobk4utjSpUvp2bMnwcHBVtujo6OxtbVlx44dlm25ubmkp6dLHN2BmoulmpoagEaj/BqNxlI5IvckcbGW3Jf0ej0uLi7s2rWLoqIiBg0aBEgs3SlmzZrFunXr+Oabbxq9tPf398fLy4udO3dattXV1bFv3z5LDLQkTmJiYti3bx91dXWWfXbu3Im3t7flpYlo+643lloiJiaG9PR0qyUId+zYgVarbXLwQNw+NDNmzJjxS/8QN0tVVRXp6ekUFhaybNkywsLCcHZ2xmAw4OzszMaNGy1vDxMTE3nuuecsa1uBuSxBrVbzxRdfEBgYiFarZfHixaxZs4bXXnsNT09P9Ho9hw4dYt26dURFRZGTk8P06dMt61+Jtu964+iC7OxsZs2axV/+8pdGa5/pdDry8vJYsGABERERVFRU8Morr+Ds7Mxzzz3XZGmyaHuuN5ZcXV1Zv349iYmJdOrUibq6OlasWMHixYt5+umnCQ4OlnvSHaI17ksrVqygrq4Og8HA1q1beemllxg7diz33XcfgMTSHWDmzJl8++23fPjhh/j6+lrmR4O5MZhKpaKhoYHPP/+c4OBgjEYj//znPykoKODVV19Fq9W2KE6CgoJYsmQJKSkphISEsH//ft566y0ef/xxq4ZQou1qjVgC87PS2bNnSU5OZseOHQwePJjCwkIcHBzQarUEBASwadMmfv75Z8LDw0lLS2PmzJncf//9DBky5Je8BOIGUyl30CS7PXv2MG7cuEbbR4wYwT//+U/mzZvHl19+SVFREV5eXjzwwAM88cQTVh0dwbyu1cKFCyktLaVjx448++yzlk6gAKWlpcyaNYstW7YAMGjQIF555RVLRzbRtrVWHM2ePZsFCxawfft2q46gF9TV1fH222+zbt06amtr6dOnD9OnT7cquxFtW2vEUkZGBu+++y779++nurqawMBAJkyYwIgRIyz7yD3p9tcasfTOO++watUqysrK8PPzY8yYMYwfP95qXpnE0u3t0heqF0yZMoWnnnoKMJcGf/zxxyxZsoSysjK6devGK6+8QlhYmGX/lsTJiRMnePXVVzl8+DCurq6MGTOGJ598UuYx3iZaK5ZeeOEFVq1a1eg48+bNo1evXoA50Z05cya7d+/Gzs6O4cOHM23atEbPXeL2ckclsUIIIYQQQggh2japSRRCCCGEEEII0WZIEiuEEEIIIYQQos2QJFYIIYQQQgghRJshSawQQgghhBBCiDZDklghhBBCCCGEEG2GJLFCCCGEEEIIIdoMSWKFEEIIIYQQQrQZksQKIYQQQgghhGgzJIkVQgghhBBCCNFm/D+McA7kFT37DgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = res.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally the distribution can be changed from the default normal to a standardized Student's T using the `distribution` property of a mean model.\n",
    "\n",
    "The Student's t distribution improves the model, and the degree of freedom is estimated to be near 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              AR - ARCH Model Results                               \n",
      "====================================================================================\n",
      "Dep. Variable:                     CPILFESL   R-squared:                       0.991\n",
      "Mean Model:                              AR   Adj. R-squared:                  0.991\n",
      "Vol Model:                             ARCH   Log-Likelihood:                142.863\n",
      "Distribution:      Standardized Student's t   AIC:                          -263.727\n",
      "Method:                  Maximum Likelihood   BIC:                          -213.370\n",
      "                                              No. Observations:                  719\n",
      "Date:                      Mon, Mar 02 2020   Df Residuals:                      708\n",
      "Time:                              11:53:30   Df Model:                           11\n",
      "                                   Mean Model                                  \n",
      "===============================================================================\n",
      "                   coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-------------------------------------------------------------------------------\n",
      "Const            0.0312  1.861e-02      1.678  9.341e-02 [-5.254e-03,6.770e-02]\n",
      "CPILFESL[1]      1.0843  3.525e-02     30.762 8.322e-208      [  1.015,  1.153]\n",
      "CPILFESL[3]     -0.0730  3.873e-02     -1.885  5.947e-02    [ -0.149,2.915e-03]\n",
      "CPILFESL[12]    -0.0236  1.316e-02     -1.791  7.331e-02 [-4.935e-02,2.224e-03]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      8.7356e-03  2.063e-03      4.235  2.284e-05 [4.693e-03,1.278e-02]\n",
      "alpha[1]       0.1715  5.064e-02      3.386  7.088e-04   [7.222e-02,  0.271]\n",
      "alpha[2]       0.2202  6.394e-02      3.443  5.743e-04   [9.485e-02,  0.345]\n",
      "alpha[3]       0.1547  6.327e-02      2.445  1.447e-02   [3.071e-02,  0.279]\n",
      "alpha[4]       0.2117  7.287e-02      2.905  3.677e-03   [6.884e-02,  0.354]\n",
      "alpha[5]       0.1959  7.853e-02      2.495  1.261e-02   [4.198e-02,  0.350]\n",
      "                              Distribution                              \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "nu             9.0448      3.366      2.687  7.204e-03 [  2.448, 15.642]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ],
   "source": [
    "from arch.univariate import StudentsT\n",
    "\n",
    "ar.distribution = StudentsT()\n",
    "res = ar.fit(update_freq=0, disp='off')\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## WTI Crude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next example uses West Texas Intermediate Crude data from FRED.  Three models are fit using alternative distributional assumptions.  The results are printed, where we can see that the normal has a much lower log-likelihood than either the Standard Student's T or the Standardized Skew Student's T -- however, these two are fairly close.  The closeness of the T and the Skew T indicate that returns are not heavily skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "normal   -18165.858870\n",
      "t        -17919.643916\n",
      "skewt    -17916.669052\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>normal</th>\n",
       "      <th>t</th>\n",
       "      <th>skewt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>0.085627</td>\n",
       "      <td>0.064980</td>\n",
       "      <td>0.064889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>0.909098</td>\n",
       "      <td>0.927950</td>\n",
       "      <td>0.928215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lambda</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.036986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mu</th>\n",
       "      <td>0.046682</td>\n",
       "      <td>0.056438</td>\n",
       "      <td>0.040928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.178652</td>\n",
       "      <td>6.186550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omega</th>\n",
       "      <td>0.055806</td>\n",
       "      <td>0.048516</td>\n",
       "      <td>0.047683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            normal         t     skewt\n",
       "alpha[1]  0.085627  0.064980  0.064889\n",
       "beta[1]   0.909098  0.927950  0.928215\n",
       "lambda         NaN       NaN -0.036986\n",
       "mu        0.046682  0.056438  0.040928\n",
       "nu             NaN  6.178652  6.186550\n",
       "omega     0.055806  0.048516  0.047683"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "import arch.data.wti\n",
    "\n",
    "crude = arch.data.wti.load()\n",
    "crude_ret = 100 * crude.DCOILWTICO.dropna().pct_change().dropna()\n",
    "res_normal = arch_model(crude_ret).fit(disp='off')\n",
    "res_t = arch_model(crude_ret, dist='t').fit(disp='off')\n",
    "res_skewt = arch_model(crude_ret, dist='skewt').fit(disp='off')\n",
    "lls = pd.Series(\n",
    "    OrderedDict((('normal', res_normal.loglikelihood),\n",
    "                 ('t', res_t.loglikelihood), ('skewt',\n",
    "                                              res_skewt.loglikelihood))))\n",
    "print(lls)\n",
    "params = pd.DataFrame(\n",
    "    OrderedDict((('normal', res_normal.params), ('t', res_t.params),\n",
    "                 ('skewt', res_skewt.params))))\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The standardized residuals can be computed by dividing the residuals by the conditional volatility.  These are plotted along with the (unstandardized, but scaled) residuals. The non-standardized residuals are more peaked in the center indicating that the distribution is somewhat more heavy tailed than that of the standardized residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "std_resid = res_normal.resid / res_normal.conditional_volatility\n",
    "unit_var_resid = res_normal.resid / res_normal.resid.std()\n",
    "df = pd.concat([std_resid, unit_var_resid], 1)\n",
    "df.columns = ['Std Resids', 'Unit Variance Resids']\n",
    "subplot = df.plot(kind='kde', xlim=(-4, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Simulation\n",
    "\n",
    "All mean models expose a method to simulate returns from assuming the\n",
    "model is correctly specified.  There are two required parameters, `params`\n",
    "which are the model parameters, and `nobs`, the number of observations\n",
    "to produce.\n",
    "\n",
    "Below we simulate from a GJR-GARCH(1,1) with Skew-t errors using parameters\n",
    "estimated on the WTI series.  The simulation returns a `DataFrame` with 3 columns:\n",
    "\n",
    "* `data`: The simulated data, which includes any mean dynamics.\n",
    "* `volatility`: The conditional volatility series\n",
    "* `errors`: The simulated errors generated to produce the model. The errors are\n",
    "  the difference between the data and its conditional mean, and can be transformed\n",
    "  into the standardized errors by dividing by the volatility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mu</th>\n",
       "      <td>0.029365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>omega</th>\n",
       "      <td>0.044375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha[1]</th>\n",
       "      <td>0.044344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gamma[1]</th>\n",
       "      <td>0.036104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beta[1]</th>\n",
       "      <td>0.931280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nu</th>\n",
       "      <td>6.211329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lambda</th>\n",
       "      <td>-0.041615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            params\n",
       "mu        0.029365\n",
       "omega     0.044375\n",
       "alpha[1]  0.044344\n",
       "gamma[1]  0.036104\n",
       "beta[1]   0.931280\n",
       "nu        6.211329\n",
       "lambda   -0.041615"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = arch_model(crude_ret, p=1, o=1, q=1, dist='skewt').fit(disp='off')\n",
    "pd.DataFrame(res.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>volatility</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.309897</td>\n",
       "      <td>2.058408</td>\n",
       "      <td>-2.339262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.114392</td>\n",
       "      <td>2.104869</td>\n",
       "      <td>-0.143758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.812156</td>\n",
       "      <td>2.042559</td>\n",
       "      <td>-1.841521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.348278</td>\n",
       "      <td>2.050008</td>\n",
       "      <td>-1.377643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.559034</td>\n",
       "      <td>2.027508</td>\n",
       "      <td>-1.588399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data  volatility    errors\n",
       "0 -2.309897    2.058408 -2.339262\n",
       "1 -0.114392    2.104869 -0.143758\n",
       "2 -1.812156    2.042559 -1.841521\n",
       "3 -1.348278    2.050008 -1.377643\n",
       "4 -1.559034    2.027508 -1.588399"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_mod = arch_model(None, p=1, o=1, q=1, dist=\"skewt\")\n",
    "\n",
    "sim_data = sim_mod.simulate(res.params, 1000)\n",
    "sim_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Simulations can be reproduced using a NumPy `RandomState`. This requires \n",
    "constructing a model from components where the `RandomState` instance is\n",
    "passed into to the distribution when the model is created.\n",
    "\n",
    "The cell below contains code that builds a model with a constant mean,\n",
    "GJR-GARCH volatility and Skew $t$ errors initialized with a user-provided\n",
    "`RandomState`. Saving the initial state allows it to be restored later so\n",
    "that the simulation can be run with the same random values.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>volatility</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.616826</td>\n",
       "      <td>4.787664</td>\n",
       "      <td>1.587461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.106756</td>\n",
       "      <td>4.637096</td>\n",
       "      <td>4.077391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.530173</td>\n",
       "      <td>4.561424</td>\n",
       "      <td>4.500807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.284819</td>\n",
       "      <td>4.507707</td>\n",
       "      <td>2.255454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.378498</td>\n",
       "      <td>4.380984</td>\n",
       "      <td>3.349133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data  volatility    errors\n",
       "0  1.616826    4.787664  1.587461\n",
       "1  4.106756    4.637096  4.077391\n",
       "2  4.530173    4.561424  4.500807\n",
       "3  2.284819    4.507707  2.255454\n",
       "4  3.378498    4.380984  3.349133"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from arch.univariate import ConstantMean, GARCH, SkewStudent\n",
    "import numpy as np\n",
    "\n",
    "rs = np.random.RandomState([892380934, 189201902, 129129894, 9890437])\n",
    "# Save the initial state to reset later\n",
    "state = rs.get_state()\n",
    "\n",
    "dist = SkewStudent(random_state=rs)\n",
    "vol = GARCH(p=1, o=1, q=1)\n",
    "repro_mod = ConstantMean(None, volatility=vol, distribution=dist)\n",
    "\n",
    "repro_mod.simulate(res.params, 1000).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Resetting the state using `set_state` shows that calling `simulate`\n",
    "using the same underlying state in the `RandomState` produces the\n",
    "same objects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>volatility</th>\n",
       "      <th>errors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.616826</td>\n",
       "      <td>4.787664</td>\n",
       "      <td>1.587461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.106756</td>\n",
       "      <td>4.637096</td>\n",
       "      <td>4.077391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.530173</td>\n",
       "      <td>4.561424</td>\n",
       "      <td>4.500807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.284819</td>\n",
       "      <td>4.507707</td>\n",
       "      <td>2.255454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.378498</td>\n",
       "      <td>4.380984</td>\n",
       "      <td>3.349133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       data  volatility    errors\n",
       "0  1.616826    4.787664  1.587461\n",
       "1  4.106756    4.637096  4.077391\n",
       "2  4.530173    4.561424  4.500807\n",
       "3  2.284819    4.507707  2.255454\n",
       "4  3.378498    4.380984  3.349133"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reset the state to the initial state\n",
    "rs.set_state(state)\n",
    "repro_mod.simulate(res.params, 1000).head()"
   ]
  }
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